Orbital Insight Energy: Oil Storage v5.1 Methodologies & Data Documentation

Size: px
Start display at page:

Download "Orbital Insight Energy: Oil Storage v5.1 Methodologies & Data Documentation"

Transcription

1 Orbital Insight Energy: Oil Storage v5.1 Methodologies & Data Documentation Overview and Summary Orbital Insight Global Oil Storage leverages commercial satellite imagery, proprietary computer vision algorithms, and advanced data science methodology to provide daily crude oil estimates at global, regional and country-specific levels. This document describes the practice and methodology we use to produce the energy dataset. The Global Oil Storage dataset presents a time series of full-volume estimates based on billions of barrels of crude oil that is stored in tens of thousands of floating-roof tanks (FRTs) around the world on a daily basis. We apply artificial intelligence to satellite imagery in order to locate and dimension each floating-roof tank, generating a database of FRTs worldwide. Using the trigonometry of FRT shadows to estimate the fill volume of oil within each tank, we calculate the aggregate volume of global oil storage. The resulting time series reflects the volume of FRTs grouped within regions of interest. Our dataset accounts for oil volume specifically stored in FRTs, excluding crude oil volume stored using other methods such as fixed-roof tanks, underground caverns, very large crude carriers (VLCCs), railcar, truck or pipeline storage. What problem does Orbital Insight Energy solve? Crude oil is one of the world s most valuable commodities but is mired by a lack of transparency, often from opaque swing producers around the world such as those within The Organization of Petroleum Exporting Countries (OPEC) and China. Storage estimates provided by organizations like the International Energy Agency (IEA) and Joint Organizations Data Initiative (JODI) are survey-based and result in delayed information (by weeks and often months) and credible reliability concerns. Orbital Insight uses a scientific approach rooted in satellite observation to bring visibility into oil storage regions across the globe. Changes in daily crude oil inventories are leading indicators of production and consumption over time, providing insight into the supply and demand forces that influence benchmark oil price changes. How does Orbital Insight Energy solve this problem? Crude Oil Storage Tank Identification & Dimensioning By leveraging information from public databases and our computer vision algorithms to locate tank farms and individual tanks, Orbital Insight has identified over 25,000 tanks worldwide representing the world s most comprehensive crude oil tank catalog. After we identify tank locations, a human worker manually labels tank images so that tank heights and diameters may be calculated. We use these dimensions to determine fill percentage as well as answer other questions regarding the maximum storage capacity of a tank farm, state, country, or geographic region.

2 Figure 1: Tank Farms in Cushing, Oklahoma with white circles marking identified FRTs (left); Individual tank metadata (right) Finding Oil Tank Farms We have developed a tank farm finder algorithm to automate the task of locating tank farms in commercial satellite imagery. The tank farms cover more than 800 Area of Interest (AOI) polygons. Once a new tank is discovered, we manually mark the center of each tank (Figure 2) and store the associated polygon in our database. Figure 2: Red dots denote the center or each FRT found within a tank farm Human Labeling for FRT Images Orbital Insight has developed an internal image-labeling tool that we use to manually mark characteristics of the FRT in order to measure the FRT dimensions. A human worker fits circles around the observed tank structure and shadows to calculate the height and diameter of the FRT. Our proprietary trigonometric projections address variations in tank orientation and sun angles. The same image-labeling tool and shadow measurement methodology is also used to estimate the tank s crude oil volume. Examples of the image-labeling tool are shown below in Figures 3 and 4.

3 Tank Dimensions for Height Measurement Figure 3: Workers label the floating roof tank by fitting circles to the bottom of the FRT and the external shadow Figure 4: Workers label the floating roof tank by fitting circles to the top and bottom of the tank as well as the internal and external shadow Volume Calculation By calculating the trigonometry of the circles fitted to the tank and tank shadows, we are able to determine the tank s dimensions and fill volumes. Total volume of a cylinder-shaped tank (shell volume) = area of circular base X tank height Maximum storage capacity of tank (net shell volume) = shell volume (roof thickness volume + base height) The filled volume of a tank is calculated in the same manner but uses the height of the floating roof instead of the shell height. We calculate the tank dimensions for all tanks and estimate the fill volume for a small proportional sample of tanks that will be used as training data for our computer vision algorithm. Data Science Methodology Orbital Insight s oil volume estimates are informed by the satellite imagery we ingest and process. We work with multiple commercial satellite imagery providers and obtain imagery in two ways: 1. We opportunistically sample provider catalogs and use every image collected that overlaps with any FRTs we track. 2. We task satellite providers and are assured imagery coverage for a given region and time. The majority of imagery used to comprise our Global Oil dataset comes from opportunistic sampling; for any given day, we are typically able to observe up to 15% of the FRT population. Orbital Insight, Inc. v

4 The number of observations available continues to grow as additional satellites launch and become operational. For the remaining percentage of tanks not observed on a given day, we use advanced data science methods to estimate changes in fill percent. Our research has shown that the population of tanks for a given geographic region tends to be heterogeneous in terms of tank size, tank fill percent and volatility. Of course, the more tanks of the population we can observe, the better our estimate of the entire population can be. At the individual tank level, we build a model of tank fill level history using observations of the tank collected at different times. When we receive a new observation that is compatible with the last observation, we average new and old observations to derive a more accurate measurement of the tank fill percent. When we receive a new observation that differs from the last observation, we replace the fill percent of the tank with the new observation. When no observations are collected for a tank, we copy-forward the previously-seen measurement estimate. Therefore, for a given daily estimate, we use observations from the past to fill-in individual tanks that were not observed. There is a trade-off between how far in the past we extend this imputation and the tanks typical volatility. The error for the imputed measurement will become larger to reflect greater uncertainty in the estimate. Before any of the observations make it to this estimation stage, we employ several filters to our collection of observations to ensure that we only use the most-trusted observations. Filters we apply to observations include: Corrupted images. Sensor glitches, oversaturated readings, etc. can lead to unusable images. Images from certain satellites that suffer from very large georegistration errors, i.e., we are not certain enough that the image is of the location the image provider claims. Observations of tanks with relatively small dimensions. The nature of our observation is such that for small tanks, both our estimate of its dimensions as well as our estimate of its fill percent suffers relatively larger errors. Clouds. Using Orbital Insight s proprietary Cloud Classifier, we are able to remove individual tank images that had clouds obscuring our view of the tank. Individual observations with low computer vision confidence scores. Our computer vision algorithms assign a score to each image which reflects how confident the algorithm is that it did well. This score can vary based on image noise, variations in image resolution, view geometry, etc. Observations for certain sun elevations. Because our method relies on shadows, for cases where the sun is too close to nadir, i.e., close to being directly above the tank, shadows become too small to reliably measure. Construction and destruction of the oil tanks. Orbital Insight has the world s largest database of oil tanks but in order to inform our modeling approach and do backtesting, we use observations over the last six (6) years. Since that period of time, new tanks have been built and some have been demolished and/or replaced. We have created an extensive catalog containing each oil tank s lifetime. We use this lifetime catalog to

5 ensure that we only use observations taken when the tank was actually present and to avoid accidentally interpreting a tank s absence as a low fill percent. After these filters have been applied, we create each daily estimate by taking the remaining observations (of tank fill percent) and computing a weighted average fill percent. We then multiply the average fill percent by the population s storage capacity to arrive at the daily estimate of the oil volume in millions of barrels. As an example, in Figure 5, we show Orbital Insight s estimate of oil volume in Cushing, OK, contrasted with an oil volume estimate from EIA. We also compute coherence and cross-correlation, not discussed here. Figure 5: Comparison of Orbital Insight's and EIA's estimates of crude oil storage in Cushing, OK Sampling Error Methodology Using our proprietary computer vision algorithms, we estimate the distribution of oil fill for each tank we observe, giving us a standard error for each tank measurement. This distribution is dependent on the satellite system and scene information. If a tank has not been observed for a few days, we propagate the error by increasing the standard error as a function of number of days since last measurement. Upon gaining a new measurement for an unobserved tank, we update the older measurement. Finally, at the region level, the standard error of individual tanks are aggregated together to obtain the region level standard error. Scaled Estimate Methodology Scaled estimates are Orbital Insight s volume estimates, renormalized to Government survey volumes (figure 6). This transformation aids in visualizing correlations in regions where Orbital Insight predictions have a lower scale than the Government survey (e.g. due to inclusion of non-frt storage in Government survey volumes). We perform renormalization by scaling the Orbital Insight volume estimate for each day such that the Orbital Insight estimated and Government survey volumes have the same mean and variance in a trailing two-year window. This is a linear transformation and does not change the correlation between Orbital Insight and Government survey volumes. Because the transformation is calculated by matching mean and

6 variance in a rolling window, the scaling transformation is slowly varying with time. This allows the transformation to automatically adjust for changes in the fraction of Government storage tracked by the Orbital Insight FRT storage volume (e.g. when new FRT storage is built). Figure 6 : Orbital Insight's Scaled Estimate and Error for PADD3 in the USA Survey Correlation Metrics Methodology In regions where Government survey data are available, we provide a survey correlation metrics table that shows the statistical relationship between Orbital Insight estimated volumes and Government survey volumes (Figure 7). Correlation metrics are computed in a trailing N year window from "Latest Survey Date". "Correlation" is the Pearson correlation coefficient between the Government survey volume and Orbital Insight estimated volume (column volume.estimate) on each survey date. "Difference Correlation" is the Pearson correlation coefficient of the first difference of Orbital Insight and Government survey volumes. First differences are computed with Orbital Insight and Government survey volumes averaged over the last one, two and four survey dates. "Hit-Rate" is the fraction of weeks in which the first difference of Orbital Insight and Government survey volumes have the same sign.

7 Figure 7 : Orbital Insight's Survey Correlation Metrics for Cushing, OK (Survey: ) Dataset Description Orbital Insight s estimates of the world s oil supply are limited to the measurement of floating-roof tanks (FRTs), excluding crude oil contained in fixed-roof tanks and other opaque sources such as underground storage. Therefore, we recommend that customers focus on the changes and trends in our time-series of available crude oil volume rather than drawing rigid conclusions on the absolute volume of oil. We assert that the crude oil volume fluctuations we observe in FRTs capture enough of the overall crude oil volume volatility to be able to produce a tradable, meaningful signal. The data we collect and use to infer from represents a sample, not a census of the population, and should be treated as such. The sample is limited by a number of factors, as outlined in this documentation. Noise should be expected and a degree of correlation/anti-correlation can be attributed to the random sample methodology. The datasets delivered to customers contains the fields described as follows: date volume.estimate smoothed.estimate Date of estimate, measurements are typically between 10am-2pm (local time) Estimate of the volume stored (millions of barrels) in the region of interest 20-day simple moving average of volume.estimate.

8 volume.estimate.stderr storage.capacity.estimate total.available.tanks sampled.tanks truth_value_mb Standard deviation of the volume.estimate in millions of barrels. We report the sampling errors associated with our measurements. Estimate of total storage capacity for the tanks in the region of interest (millions of barrels) Total number of floating roof tanks in the region of interest Total number of individual tanks observed at least once in the last 15 days Ground truth, if available, from other sources such as Government surveys (eg, U.S. EIA) An example view of the data (CSV) is shown below: Dataset Delivery The output of the time series includes the estimated daily volume of oil (in millions of barrels) aggregated at the country, region or global level, and the corresponding date of the estimate. The time series can be viewed and downloaded through any of the following methods: 1. Orbital Insight s Application Programming Interface (API): *.csv file 2. Orbital Insight s Web Application: graphical User Interface (GUI), *.csv, *.json files 3. Using a Chicago Mercantile Exchange DataMine account: *.csv file Individual tank level data ( raw data ) is also be available as an additional option.

Imaging the whole Earth every day. Josh Alban VP, Business Development

Imaging the whole Earth every day. Josh Alban VP, Business Development Imaging the whole Earth every day Josh Alban VP, Business Development josh@planet.com Planet Labs helps you detect and act on change by imaging the whole Earth everyday. Challenges Limited Coverage Low

More information

AN INTERNATIONAL SOLAR IRRADIANCE DATA INGEST SYSTEM FOR FORECASTING SOLAR POWER AND AGRICULTURAL CROP YIELDS

AN INTERNATIONAL SOLAR IRRADIANCE DATA INGEST SYSTEM FOR FORECASTING SOLAR POWER AND AGRICULTURAL CROP YIELDS AN INTERNATIONAL SOLAR IRRADIANCE DATA INGEST SYSTEM FOR FORECASTING SOLAR POWER AND AGRICULTURAL CROP YIELDS James Hall JHTech PO Box 877 Divide, CO 80814 Email: jameshall@jhtech.com Jeffrey Hall JHTech

More information

Data Aggregation with InfraWorks and ArcGIS for Visualization, Analysis, and Planning

Data Aggregation with InfraWorks and ArcGIS for Visualization, Analysis, and Planning Data Aggregation with InfraWorks and ArcGIS for Visualization, Analysis, and Planning Stephen Brockwell President, Brockwell IT Consulting, Inc. Join the conversation #AU2017 KEYWORD Class Summary Silos

More information

Machine Learning to Automatically Detect Human Development from Satellite Imagery

Machine Learning to Automatically Detect Human Development from Satellite Imagery Technical Disclosure Commons Defensive Publications Series April 24, 2017 Machine Learning to Automatically Detect Human Development from Satellite Imagery Matthew Manolides Follow this and additional

More information

When Map Quality Matters

When Map Quality Matters When Map Quality Matters 50% 25% Powerful geospatial mapping tools for Adobe Creative Cloud and offline map solutions for mobile devices 20% When Map Quality Matters 10% We re focused on creating powerful

More information

RS Metrics CME Group Copper Futures Price Predictive Analysis Explained

RS Metrics CME Group Copper Futures Price Predictive Analysis Explained RS Metrics CME Group Copper Futures Price Predictive Analysis Explained Disclaimer ALL DATA REFERENCED IN THIS WHITE PAPER IS PROVIDED AS IS, AND RS METRICS DOES NOT MAKE AND HEREBY EXPRESSLY DISCLAIMS,

More information

Land-Line Technical information leaflet

Land-Line Technical information leaflet Land-Line Technical information leaflet The product Land-Line is comprehensive and accurate large-scale digital mapping available for Great Britain. It comprises nearly 229 000 separate map tiles of data

More information

Introduction to ArcGIS GeoAnalytics Server. Sarah Ambrose & Noah Slocum

Introduction to ArcGIS GeoAnalytics Server. Sarah Ambrose & Noah Slocum Introduction to ArcGIS GeoAnalytics Server Sarah Ambrose & Noah Slocum Agenda Overview Analysis Capabilities + Demo Deployment and Configuration Questions ArcGIS GeoAnalytics Server uses the power of distributed

More information

GEOGRAPHIC INFORMATION SYSTEMS Session 8

GEOGRAPHIC INFORMATION SYSTEMS Session 8 GEOGRAPHIC INFORMATION SYSTEMS Session 8 Introduction Geography underpins all activities associated with a census Census geography is essential to plan and manage fieldwork as well as to report results

More information

Features and Benefits

Features and Benefits Autodesk LandXplorer Features and Benefits Use the Autodesk LandXplorer software family to help improve decision making, lower costs, view and share changes, and avoid the expense of creating physical

More information

WeatherHawk Weather Station Protocol

WeatherHawk Weather Station Protocol WeatherHawk Weather Station Protocol Purpose To log atmosphere data using a WeatherHawk TM weather station Overview A weather station is setup to measure and record atmospheric measurements at 15 minute

More information

Geo-Spatial Technologies Application To Customs

Geo-Spatial Technologies Application To Customs Geo-Spatial Technologies Application To Customs Hammamet September 26 th, 2017 GE-Data: previous experience Agenda Leveraging data through geography Geomatics GIS databases Information products and tools

More information

1.6 TRENDS AND VARIABILITY OF SNOWFALL AND SNOW COVER ACROSS NORTH AMERICA AND EURASIA. PART 2: WHAT THE DATA SAY

1.6 TRENDS AND VARIABILITY OF SNOWFALL AND SNOW COVER ACROSS NORTH AMERICA AND EURASIA. PART 2: WHAT THE DATA SAY 1.6 TRENDS AND VARIABILITY OF SNOWFALL AND SNOW COVER ACROSS NORTH AMERICA AND EURASIA. PART 2: WHAT THE DATA SAY David A. Robinson* Rutgers University, Department of Geography, Piscataway, New Jersey

More information

AUTOMATED METERED WATER CONSUMPTION ANALYSIS

AUTOMATED METERED WATER CONSUMPTION ANALYSIS AUTOMATED METERED WATER CONSUMPTION ANALYSIS Shane Zhong 1, Nick Turich 1, Patrick Hayde 1 1. Treatment and Network Planning, SA Water, Adelaide, SA, Australia ABSTRACT Water utilities collect and store

More information

SpaceNet Round 2: Automated Mapping Using Satellite Imagery Accelerated by Deep Learning

SpaceNet Round 2: Automated Mapping Using Satellite Imagery Accelerated by Deep Learning SpaceNet Round 2: Automated Mapping Using Satellite Imagery Accelerated by Deep Learning David Lindenbaum, SpaceNet Lead, CosmiQ Works, an IQT Lab Todd M. Bacastow, SpaceNet Lead, DigitalGlobe Radiant

More information

Bentley Map Advancing GIS for the World s Infrastructure

Bentley Map Advancing GIS for the World s Infrastructure Bentley Map Advancing GIS for the World s Infrastructure Presentation Overview Why would you need Bentley Map? What is Bentley Map? Where is Bentley Map Used? Why would you need Bentley Map? Because your

More information

on space debris objects obtained by the

on space debris objects obtained by the KIAM space debris data center for processing and analysis of information on space debris objects obtained by the ISON network Vladimir Agapov, Igor Molotov Keldysh Institute of Applied Mathematics RAS

More information

Visualizing Energy Usage and Consumption of the World

Visualizing Energy Usage and Consumption of the World Visualizing Energy Usage and Consumption of the World William Liew Abstract As the world becomes more and more environmentally aware, a simple layman s visualization of worldwide energy use is needed to

More information

Renewable Energy Development and Airborne Wildlife Conservation

Renewable Energy Development and Airborne Wildlife Conservation Whitepaper ECHOTRACK TM RADAR ACOUSTIC TM SURVEILLANCE SYSTEM Renewable Energy Development and Airborne Wildlife Conservation Renewable energy developers must meet regulatory requirements to mitigate for

More information

The Global Statistical Geospatial Framework and the Global Fundamental Geospatial Themes

The Global Statistical Geospatial Framework and the Global Fundamental Geospatial Themes The Global Statistical Geospatial Framework and the Global Fundamental Geospatial Themes Sub-regional workshop on integration of administrative data, big data and geospatial information for the compilation

More information

CentropeSTATISTICS Working Interactively with Cross-Border Statistic Data Clemens Beyer, Walter Pozarek, Manfred Schrenk

CentropeSTATISTICS Working Interactively with Cross-Border Statistic Data Clemens Beyer, Walter Pozarek, Manfred Schrenk Clemens Beyer, Walter Pozarek, Manfred Schrenk (Dipl.-Ing. Clemens Beyer, CEIT ALANOVA, Concorde Business Park 2/F, 2320 Schwechat, Austria, c.beyer@ceit.at) (Dipl.-Ing. Walter Pozarek, PGO Planungsgemeinschaft

More information

Globally Estimating the Population Characteristics of Small Geographic Areas. Tom Fitzwater

Globally Estimating the Population Characteristics of Small Geographic Areas. Tom Fitzwater Globally Estimating the Population Characteristics of Small Geographic Areas Tom Fitzwater U.S. Census Bureau Population Division What we know 2 Where do people live? Difficult to measure and quantify.

More information

Some Personal Perspectives on Demand Forecasting Past, Present, Future

Some Personal Perspectives on Demand Forecasting Past, Present, Future Some Personal Perspectives on Demand Forecasting Past, Present, Future Hans Levenbach, PhD Delphus, Inc. INFORMS Luncheon Penn Club, NYC Presentation Overview Introduction Demand Analysis and Forecasting

More information

Analytical data, the web, and standards for unified laboratory informatics databases

Analytical data, the web, and standards for unified laboratory informatics databases Analytical data, the web, and standards for unified laboratory informatics databases Presented By Patrick D. Wheeler & Graham A. McGibbon ACS San Diego 16 March, 2016 Sources Process, Analyze Interfaces,

More information

Quality and Coverage of Data Sources

Quality and Coverage of Data Sources Quality and Coverage of Data Sources Objectives Selecting an appropriate source for each item of information to be stored in the GIS database is very important for GIS Data Capture. Selection of quality

More information

Data Aggregation with InfraWorks and ArcGIS for Visualization, Analysis, and Planning

Data Aggregation with InfraWorks and ArcGIS for Visualization, Analysis, and Planning CI125230 Data Aggregation with InfraWorks and ArcGIS for Visualization, Analysis, and Planning Stephen Brockwell Brockwell IT Consulting Inc. Sean Kinahan Brockwell IT Consulting Inc. Learning Objectives

More information

GIS Functions and Integration. Tyler Pauley Associate Consultant

GIS Functions and Integration. Tyler Pauley Associate Consultant GIS Functions and Integration Tyler Pauley Associate Consultant Contents GIS in AgileAssets products Displaying data within AMS Symbolizing the map display Display on Bing Maps Demo- Displaying a map in

More information

3D Urban Information Models in making a smart city the i-scope project case study

3D Urban Information Models in making a smart city the i-scope project case study UDC: 007:528.9]:004; 007:912]:004; 004.92 DOI: 10.14438/gn.2014.17 Typology: 1.04 Professional Article 3D Urban Information Models in making a smart city the i-scope project case study Dragutin PROTIĆ

More information

User Manuel. EurotaxForecast. Version Latest changes ( )

User Manuel. EurotaxForecast. Version Latest changes ( ) User Manuel EurotaxForecast Version 1.23.0771- Latest changes (19.07.2003) Contents Preface 5 Welcome to Eurotax Forecast...5 Using this manual 6 How to use this manual?...6 Program overview 7 General

More information

Spatial Data Infrastructure Concepts and Components. Douglas Nebert U.S. Federal Geographic Data Committee Secretariat

Spatial Data Infrastructure Concepts and Components. Douglas Nebert U.S. Federal Geographic Data Committee Secretariat Spatial Data Infrastructure Concepts and Components Douglas Nebert U.S. Federal Geographic Data Committee Secretariat August 2009 What is a Spatial Data Infrastructure (SDI)? The SDI provides a basis for

More information

Hidden Markov Models. Hal Daumé III. Computer Science University of Maryland CS 421: Introduction to Artificial Intelligence 19 Apr 2012

Hidden Markov Models. Hal Daumé III. Computer Science University of Maryland CS 421: Introduction to Artificial Intelligence 19 Apr 2012 Hidden Markov Models Hal Daumé III Computer Science University of Maryland me@hal3.name CS 421: Introduction to Artificial Intelligence 19 Apr 2012 Many slides courtesy of Dan Klein, Stuart Russell, or

More information

M E R C E R W I N WA L K T H R O U G H

M E R C E R W I N WA L K T H R O U G H H E A L T H W E A L T H C A R E E R WA L K T H R O U G H C L I E N T S O L U T I O N S T E A M T A B L E O F C O N T E N T 1. Login to the Tool 2 2. Published reports... 7 3. Select Results Criteria...

More information

HISTORY OF GIS AND ESRI

HISTORY OF GIS AND ESRI HISTORY OF GIS AND ESRI First Developed by Dr. Roger Tomlinson in Canada 1960 (CGIS Canadian geographic system) The system was designed to inventory land use and assist in the management of natural resources

More information

Introduction. Project Summary In 2014 multiple local Otsego county agencies, Otsego County Soil and Water

Introduction. Project Summary In 2014 multiple local Otsego county agencies, Otsego County Soil and Water Introduction Project Summary In 2014 multiple local Otsego county agencies, Otsego County Soil and Water Conservation District (SWCD), the Otsego County Planning Department (OPD), and the Otsego County

More information

DATA SOURCES AND INPUT IN GIS. By Prof. A. Balasubramanian Centre for Advanced Studies in Earth Science, University of Mysore, Mysore

DATA SOURCES AND INPUT IN GIS. By Prof. A. Balasubramanian Centre for Advanced Studies in Earth Science, University of Mysore, Mysore DATA SOURCES AND INPUT IN GIS By Prof. A. Balasubramanian Centre for Advanced Studies in Earth Science, University of Mysore, Mysore 1 1. GIS stands for 'Geographic Information System'. It is a computer-based

More information

Situational Report HOW PLANET MONITORS DEVELOPMENTS IN THE SOUTH CHINA SEA AUGUST 1, 2016 IMAGE ACQUIRED: JULY 22, 2015 PLANET.

Situational Report HOW PLANET MONITORS DEVELOPMENTS IN THE SOUTH CHINA SEA AUGUST 1, 2016 IMAGE ACQUIRED: JULY 22, 2015 PLANET. Situational Report IMAGE ACQUIRED: JULY 22, 2015 HOW PLANET MONITORS DEVELOPMENTS IN THE SOUTH CHINA SEA AUGUST 1, 2016 PRESS@PLANET.COM PLANET.COM THE SITUATION An international tribunal in The Hague

More information

Enabling Success in Enterprise Asset Management: Case Study for Developing and Integrating GIS with CMMS for a Large WWTP

Enabling Success in Enterprise Asset Management: Case Study for Developing and Integrating GIS with CMMS for a Large WWTP Enabling Success in Enterprise Asset Management: Case Study for Developing and Integrating GIS with CMMS for a Large WWTP Allison Blake, P.E. 1*, Matthew Jalbert, P.E. 2, Julia J. Hunt, P.E. 2, Mazen Kawasmi,

More information

SuperPack North America

SuperPack North America SuperPack North America Speedwell SuperPack makes available an unprecedented range of quality historical weather data, and weather data feeds for a single annual fee. SuperPack dramatically simplifies

More information

ASSESSMENT. Industry Solutions Harness the Power of GIS for Property Assessment

ASSESSMENT. Industry Solutions Harness the Power of GIS for Property Assessment ASSESSMENT Industry Solutions Harness the Power of GIS for Property Assessment Esri Canada has thousands of customers worldwide who are using the transforming power of GIS technology to collect, maintain,

More information

CS 188: Artificial Intelligence Spring Announcements

CS 188: Artificial Intelligence Spring Announcements CS 188: Artificial Intelligence Spring 2011 Lecture 18: HMMs and Particle Filtering 4/4/2011 Pieter Abbeel --- UC Berkeley Many slides over this course adapted from Dan Klein, Stuart Russell, Andrew Moore

More information

Demand Forecasting. for. Microsoft Dynamics 365 for Operations. User Guide. Release 7.1. April 2018

Demand Forecasting. for. Microsoft Dynamics 365 for Operations. User Guide. Release 7.1. April 2018 Demand Forecasting for Microsoft Dynamics 365 for Operations User Guide Release 7.1 April 2018 2018 Farsight Solutions Limited All Rights Reserved. Portions copyright Business Forecast Systems, Inc. This

More information

Lecture 5. Symbolization and Classification MAP DESIGN: PART I. A picture is worth a thousand words

Lecture 5. Symbolization and Classification MAP DESIGN: PART I. A picture is worth a thousand words Lecture 5 MAP DESIGN: PART I Symbolization and Classification A picture is worth a thousand words Outline Symbolization Types of Maps Classifying Features Visualization Considerations Symbolization Symbolization

More information

Web Visualization of Geo-Spatial Data using SVG and VRML/X3D

Web Visualization of Geo-Spatial Data using SVG and VRML/X3D Web Visualization of Geo-Spatial Data using SVG and VRML/X3D Jianghui Ying Falls Church, VA 22043, USA jying@vt.edu Denis Gračanin Blacksburg, VA 24061, USA gracanin@vt.edu Chang-Tien Lu Falls Church,

More information

NR402 GIS Applications in Natural Resources

NR402 GIS Applications in Natural Resources NR402 GIS Applications in Natural Resources Lesson 1 Introduction to GIS Eva Strand, University of Idaho Map of the Pacific Northwest from http://www.or.blm.gov/gis/ Welcome to NR402 GIS Applications in

More information

Rick Ebert & Joseph Mazzarella For the NED Team. Big Data Task Force NASA, Ames Research Center 2016 September 28-30

Rick Ebert & Joseph Mazzarella For the NED Team. Big Data Task Force NASA, Ames Research Center 2016 September 28-30 NED Mission: Provide a comprehensive, reliable and easy-to-use synthesis of multi-wavelength data from NASA missions, published catalogs, and the refereed literature, to enhance and enable astrophysical

More information

Spatial Analysis with Web GIS. Rachel Weeden

Spatial Analysis with Web GIS. Rachel Weeden Spatial Analysis with Web GIS Rachel Weeden Agenda Subhead goes here Introducing ArcGIS Online Spatial Analysis Workflows Scenarios Other Options Resources ArcGIS is a Platform Making mapping and analytics

More information

Welcome to NR502 GIS Applications in Natural Resources. You can take this course for 1 or 2 credits. There is also an option for 3 credits.

Welcome to NR502 GIS Applications in Natural Resources. You can take this course for 1 or 2 credits. There is also an option for 3 credits. Welcome to NR502 GIS Applications in Natural Resources. You can take this course for 1 or 2 credits. There is also an option for 3 credits. The 1st credit consists of a series of readings, demonstration,

More information

ArcGIS GeoAnalytics Server: An Introduction. Sarah Ambrose and Ravi Narayanan

ArcGIS GeoAnalytics Server: An Introduction. Sarah Ambrose and Ravi Narayanan ArcGIS GeoAnalytics Server: An Introduction Sarah Ambrose and Ravi Narayanan Overview Introduction Demos Analysis Concepts using GeoAnalytics Server GeoAnalytics Data Sources GeoAnalytics Server Administration

More information

CAD: Introduction to using CAD Data in ArcGIS. Kyle Williams & Jeff Reinhart

CAD: Introduction to using CAD Data in ArcGIS. Kyle Williams & Jeff Reinhart CAD: Introduction to using CAD Data in ArcGIS Kyle Williams & Jeff Reinhart What we will accomplish today Overview of ArcGIS CAD Support Georeferencing CAD data for ArcGIS How Mapping Specification for

More information

DRIVING ROI. The Business Case for Advanced Weather Solutions for the Energy Market

DRIVING ROI. The Business Case for Advanced Weather Solutions for the Energy Market DRIVING ROI The Business Case for Advanced Weather Solutions for the Energy Market Table of Contents Energy Trading Challenges 3 Skill 4 Speed 5 Precision 6 Key ROI Findings 7 About The Weather Company

More information

Weather Technology in the Cockpit (WTIC) Program Program Update. Friends/Partners of Aviation Weather (FPAW) November 2, 2016

Weather Technology in the Cockpit (WTIC) Program Program Update. Friends/Partners of Aviation Weather (FPAW) November 2, 2016 Weather Technology in the Cockpit (WTIC) Program Program Update Friends/Partners of Aviation Weather (FPAW) November 2, 2016 Presented by Gary Pokodner, WTIC Program Manager Phone: 202.267.2786 Email:

More information

Reimaging GIS: Geographic Information Society. Clint Brown Linda Beale Mark Harrower Esri

Reimaging GIS: Geographic Information Society. Clint Brown Linda Beale Mark Harrower Esri Reimaging GIS: Geographic Information Society Clint Brown Linda Beale Mark Harrower Esri 8 billion = Number of basemap requests per month on AGOL 14,000 = Unique requests per second 12,000 = New Items

More information

Introducing GIS analysis

Introducing GIS analysis 1 Introducing GIS analysis GIS analysis lets you see patterns and relationships in your geographic data. The results of your analysis will give you insight into a place, help you focus your actions, or

More information

Understanding the Differences between LS Algorithms and Sequential Filters

Understanding the Differences between LS Algorithms and Sequential Filters Understanding the Differences between LS Algorithms and Sequential Filters In order to perform meaningful comparisons between outputs from a least squares (LS) orbit determination algorithm and orbit determination

More information

USE OF RADIOMETRICS IN SOIL SURVEY

USE OF RADIOMETRICS IN SOIL SURVEY USE OF RADIOMETRICS IN SOIL SURVEY Brian Tunstall 2003 Abstract The objectives and requirements with soil mapping are summarised. The capacities for different methods to address these objectives and requirements

More information

Jay Lawrimore NOAA National Climatic Data Center 9 October 2013

Jay Lawrimore NOAA National Climatic Data Center 9 October 2013 Jay Lawrimore NOAA National Climatic Data Center 9 October 2013 Daily data GHCN-Daily as the GSN Archive Monthly data GHCN-Monthly and CLIMAT messages International Surface Temperature Initiative Global

More information

Lightcloud Application

Lightcloud Application Controlling Your Lightcloud System Lightcloud Application Lightcloud Application Navigating the Application Devices Device Settings Organize Control Energy Scenes Schedules Demand Response Power Up State

More information

WeatherCloud Hyper-Local Global Forecasting All rights reserved. Fathym, Inc.

WeatherCloud Hyper-Local Global Forecasting All rights reserved. Fathym, Inc. WeatherCloud Hyper-Local Global Forecasting based on current forecast techniques EVOLVING FORECASTING TECHNOLOGY 1) The WeatherCloud backend forecast system allows for routing around hazardous weather

More information

SADA General Information

SADA General Information SADA General Information Windows--based freeware designed to integrate scientific models with decision and cost analysis frameworks in a seamless, easy to use environment. Visualization/GIS Custom Analysis

More information

Geoscape Capturing Australia s Built Environment for emergency modelling and management. Dan Paull Chief Executive Officer PSMA Australia

Geoscape Capturing Australia s Built Environment for emergency modelling and management. Dan Paull Chief Executive Officer PSMA Australia Geoscape Capturing Australia s Built Environment for emergency modelling and management Dan Paull Chief Executive Officer PSMA Australia There is no wealth like knowledge, and no poverty like ignorance.

More information

Benefits of Applying Predictive Intelligence to the Space Situational Awareness (SSA) Mission

Benefits of Applying Predictive Intelligence to the Space Situational Awareness (SSA) Mission Benefits of Applying Predictive Intelligence to the Space Situational Awareness (SSA) Mission Abstract Ben Lane and Brian Mann Military Civil Space and Ground (MCS&G) Northrop Grumman Electronic Systems

More information

PROBA-V CLOUD MASK VALIDATION

PROBA-V CLOUD MASK VALIDATION Version: 1 Page 1 PROBA-V CLOUD MASK VALIDATION Validation Report Version 1.0 Kerstin Stelzer, Michael Paperin, Grit Kirches, Carsten Brockmann 25.04.2016 Version: 1 Page 2 Table of content Abbreviations

More information

SIMULATION AND OPTIMISATION OF AN OF OPTICAL REMOTE SENSING SYSTEM FOR MONITORING THE EUROPEAN GAS PIPELINE NETWORK

SIMULATION AND OPTIMISATION OF AN OF OPTICAL REMOTE SENSING SYSTEM FOR MONITORING THE EUROPEAN GAS PIPELINE NETWORK SIMULATION AND OPTIMISATION OF AN OF OPTICAL REMOTE SENSING SYSTEM FOR MONITORING THE EUROPEAN GAS PIPELINE NETWORK M. van Persie, A. van der Kamp, T. Algra National Aerospace Laboratory NLR, The Netherlands

More information

Louisiana Transportation Engineering Conference. Monday, February 12, 2007

Louisiana Transportation Engineering Conference. Monday, February 12, 2007 Louisiana Transportation Engineering Conference Monday, February 12, 2007 Agenda Project Background Goal of EIS Why Use GIS? What is GIS? How used on this Project Other site selection tools I-69 Corridor

More information

These modules are covered with a brief information and practical in ArcGIS Software and open source software also like QGIS, ILWIS.

These modules are covered with a brief information and practical in ArcGIS Software and open source software also like QGIS, ILWIS. Online GIS Training and training modules covered are: 1. ArcGIS, Analysis, Fundamentals and Implementation 2. ArcGIS Web Data Sharing 3. ArcGIS for Desktop 4. ArcGIS for Server These modules are covered

More information

New Land Cover & Land Use Data for the Chesapeake Bay Watershed

New Land Cover & Land Use Data for the Chesapeake Bay Watershed New Land Cover & Land Use Data for the Chesapeake Bay Watershed Why? The Chesapeake Bay Program (CBP) partnership is in the process of improving and refining the Phase 6 suite of models used to inform

More information

You are Building Your Organization s Geographic Knowledge

You are Building Your Organization s Geographic Knowledge You are Building Your Organization s Geographic Knowledge And Increasingly Making it Available Sharing Data Publishing Maps and Geo-Apps Developing Collaborative Approaches Citizens Knowledge Workers Analysts

More information

ArcGIS. for Server. Understanding our World

ArcGIS. for Server. Understanding our World ArcGIS for Server Understanding our World ArcGIS for Server Create, Distribute, and Manage GIS Services You can use ArcGIS for Server to create services from your mapping and geographic information system

More information

Display data in a map-like format so that geographic patterns and interrelationships are visible

Display data in a map-like format so that geographic patterns and interrelationships are visible Vilmaliz Rodríguez Guzmán M.S. Student, Department of Geology University of Puerto Rico at Mayagüez Remote Sensing and Geographic Information Systems (GIS) Reference: James B. Campbell. Introduction to

More information

Search for the Dubai in the remap search bar:

Search for the Dubai in the remap search bar: This tutorial is aimed at developing maps for two time periods with in Remap (). In this tutorial we are going to develop a classification water and non-water in Dubai for the year 2000 and the year 2016.

More information

Using CAD data in ArcGIS

Using CAD data in ArcGIS Using CAD data in ArcGIS Phil Sanchez and Jeff Reinhart Esri UC 2014 Technical Workshop Agenda Overview of ArcGIS CAD Support Using CAD Datasets in ArcMap Georeferencing CAD data for ArcGIS Loading CAD

More information

<Insert Picture Here> Oracle Spatial 11g. Dr. Siva Ravada

<Insert Picture Here> Oracle Spatial 11g. Dr. Siva Ravada Oracle Spatial 11g Dr. Siva Ravada New in Oracle Spatial 11g 3D Support Spatial Web Services Network Data Model GeoRaster Performance Improvements 3D Applications Location-based services

More information

Introduction to the 176A labs and ArcGIS

Introduction to the 176A labs and ArcGIS Introduction to the 176A labs and ArcGIS Acknowledgement: Slides by David Maidment, U Texas-Austin and Francisco Olivera (TAMU) Purpose of the labs Hands-on experience with one software pakage Introduction

More information

SPOT DEM Product Description

SPOT DEM Product Description SPOT DEM Product Description Version 1.1 - May 1 st, 2004 This edition supersedes previous versions Acronyms DIMAP DTED DXF HRS JPEG, JPG DEM SRTM SVG Tiff - GeoTiff XML Digital Image MAP encapsulation

More information

The AMGI project: A Brief Overview

The AMGI project: A Brief Overview The AMGI project: A Brief Overview World Bank Group (Energy & Extractives Global Practice) GEEDR Francisco Igualada (figualada@worldbank.org) Presented by Ash Johnson, Geosoft Inc. The AMGI Project: Vision

More information

Tutorial 8 Raster Data Analysis

Tutorial 8 Raster Data Analysis Objectives Tutorial 8 Raster Data Analysis This tutorial is designed to introduce you to a basic set of raster-based analyses including: 1. Displaying Digital Elevation Model (DEM) 2. Slope calculations

More information

Among various open-source GIS programs, QGIS can be the best suitable option which can be used across partners for reasons outlined below.

Among various open-source GIS programs, QGIS can be the best suitable option which can be used across partners for reasons outlined below. Comparison of Geographic Information Systems (GIS) software As of January 2018, WHO has reached an agreement with ESRI (an international supplier of GIS software) for an unlimited use of ArcGIS Desktop

More information

Esri UC2013. Technical Workshop.

Esri UC2013. Technical Workshop. Esri International User Conference San Diego, California Technical Workshops July 9, 2013 CAD: Introduction to using CAD Data in ArcGIS Jeff Reinhart & Phil Sanchez Agenda Overview of ArcGIS CAD Support

More information

ArcGIS Enterprise: What s New. Philip Heede Shannon Kalisky Melanie Summers Sam Williamson

ArcGIS Enterprise: What s New. Philip Heede Shannon Kalisky Melanie Summers Sam Williamson ArcGIS Enterprise: What s New Philip Heede Shannon Kalisky Melanie Summers Sam Williamson ArcGIS Enterprise is the new name for ArcGIS for Server What is ArcGIS Enterprise ArcGIS Enterprise is powerful

More information

A BASE SYSTEM FOR MICRO TRAFFIC SIMULATION USING THE GEOGRAPHICAL INFORMATION DATABASE

A BASE SYSTEM FOR MICRO TRAFFIC SIMULATION USING THE GEOGRAPHICAL INFORMATION DATABASE A BASE SYSTEM FOR MICRO TRAFFIC SIMULATION USING THE GEOGRAPHICAL INFORMATION DATABASE Yan LI Ritsumeikan Asia Pacific University E-mail: yanli@apu.ac.jp 1 INTRODUCTION In the recent years, with the rapid

More information

About Nnergix +2, More than 2,5 GW forecasted. Forecasting in 5 countries. 4 predictive technologies. More than power facilities

About Nnergix +2, More than 2,5 GW forecasted. Forecasting in 5 countries. 4 predictive technologies. More than power facilities About Nnergix +2,5 5 4 +20.000 More than 2,5 GW forecasted Forecasting in 5 countries 4 predictive technologies More than 20.000 power facilities Nnergix s Timeline 2012 First Solar Photovoltaic energy

More information

Brazil Paper for the. Second Preparatory Meeting of the Proposed United Nations Committee of Experts on Global Geographic Information Management

Brazil Paper for the. Second Preparatory Meeting of the Proposed United Nations Committee of Experts on Global Geographic Information Management Brazil Paper for the Second Preparatory Meeting of the Proposed United Nations Committee of Experts on Global Geographic Information Management on Data Integration Introduction The quick development of

More information

The File Geodatabase API. Craig Gillgrass Lance Shipman

The File Geodatabase API. Craig Gillgrass Lance Shipman The File Geodatabase API Craig Gillgrass Lance Shipman Schedule Cell phones and pagers Please complete the session survey we take your feedback very seriously! Overview File Geodatabase API - Introduction

More information

Managing Imagery and Raster Data Using Mosaic Datasets

Managing Imagery and Raster Data Using Mosaic Datasets Esri International User Conference San Diego, California Technical Workshops July 25, 2012 Managing Imagery and Raster Data Using Mosaic Datasets Hong Xu, Prashant Mangtani Presentation Overview Introduction

More information

SAFNWC/MSG SEVIRI CLOUD PRODUCTS

SAFNWC/MSG SEVIRI CLOUD PRODUCTS SAFNWC/MSG SEVIRI CLOUD PRODUCTS M. Derrien and H. Le Gléau Météo-France / DP / Centre de Météorologie Spatiale BP 147 22302 Lannion. France ABSTRACT Within the SAF in support to Nowcasting and Very Short

More information

A Broad View of Geospatial Technology & Systems

A Broad View of Geospatial Technology & Systems A Broad View of Geospatial Technology & Systems Pete Large Vice President, Trimble On the shoulders of giants 1 Since their time, our ability to generate geospatial information has grown exponentially

More information

Identified a possible new offset location where the customer is currently exploring drill options.

Identified a possible new offset location where the customer is currently exploring drill options. GroundMetrics was hired to conduct a Full-Field Resistivity Survey for an oil and gas producer that needed to make crucial decisions to drive profitability at the location. The results saved them hundreds

More information

Integrated Electricity Demand and Price Forecasting

Integrated Electricity Demand and Price Forecasting Integrated Electricity Demand and Price Forecasting Create and Evaluate Forecasting Models The many interrelated factors which influence demand for electricity cannot be directly modeled by closed-form

More information

Cerno Application Note Extending the Limits of Mass Spectrometry

Cerno Application Note Extending the Limits of Mass Spectrometry Creation of Accurate Mass Library for NIST Database Search Novel MS calibration has been shown to enable accurate mass and elemental composition determination on quadrupole GC/MS systems for either molecular

More information

Integrating ARCGIS with Datamining Software to Predict Habitat for Red Sea Urchins on the Coast of British Columbia.

Integrating ARCGIS with Datamining Software to Predict Habitat for Red Sea Urchins on the Coast of British Columbia. Integrating ARCGIS with Datamining Software to Predict Habitat for Red Sea Urchins on the Coast of British Columbia. Wayne Hajas Pacific Biological Station Nanaimo, BC 1 Allison Smeaton GIS-student intern

More information

Announcements. CS 188: Artificial Intelligence Fall VPI Example. VPI Properties. Reasoning over Time. Markov Models. Lecture 19: HMMs 11/4/2008

Announcements. CS 188: Artificial Intelligence Fall VPI Example. VPI Properties. Reasoning over Time. Markov Models. Lecture 19: HMMs 11/4/2008 CS 88: Artificial Intelligence Fall 28 Lecture 9: HMMs /4/28 Announcements Midterm solutions up, submit regrade requests within a week Midterm course evaluation up on web, please fill out! Dan Klein UC

More information

Markov Models. CS 188: Artificial Intelligence Fall Example. Mini-Forward Algorithm. Stationary Distributions.

Markov Models. CS 188: Artificial Intelligence Fall Example. Mini-Forward Algorithm. Stationary Distributions. CS 88: Artificial Intelligence Fall 27 Lecture 2: HMMs /6/27 Markov Models A Markov model is a chain-structured BN Each node is identically distributed (stationarity) Value of X at a given time is called

More information

MeteoGroup RoadMaster. The world s leading winter road weather solution

MeteoGroup RoadMaster. The world s leading winter road weather solution MeteoGroup RoadMaster The world s leading winter road weather solution Discover why RoadMaster is the world s leading winter road weather solution. Managing winter road maintenance means that you carry

More information

TOWARDS THE DEVELOPMENT OF A MONITORING SYSTEM FOR PLANNING POLICY Residential Land Uses Case study of Brisbane, Melbourne, Chicago and London

TOWARDS THE DEVELOPMENT OF A MONITORING SYSTEM FOR PLANNING POLICY Residential Land Uses Case study of Brisbane, Melbourne, Chicago and London TOWARDS THE DEVELOPMENT OF A MONITORING SYSTEM FOR PLANNING POLICY Residential Land Uses Case study of Brisbane, Melbourne, Chicago and London Presented to CUPUM 12 July 2017 by Claire Daniel Urban Planning/Data

More information

CPSC 695. Future of GIS. Marina L. Gavrilova

CPSC 695. Future of GIS. Marina L. Gavrilova CPSC 695 Future of GIS Marina L. Gavrilova The future of GIS Overview What is GIS now How GIS was viewed before Current trends and developments Future directions of research What is GIS? Internet's definition

More information

Measuring earthquake-generated surface offsets from high-resolution digital topography

Measuring earthquake-generated surface offsets from high-resolution digital topography Measuring earthquake-generated surface offsets from high-resolution digital topography July 19, 2011 David E. Haddad david.e.haddad@asu.edu Active Tectonics, Quantitative Structural Geology, and Geomorphology

More information

Enabling ENVI. ArcGIS for Server

Enabling ENVI. ArcGIS for Server Enabling ENVI throughh ArcGIS for Server 1 Imagery: A Unique and Valuable Source of Data Imagery is not just a base map, but a layer of rich information that can address problems faced by GIS users. >

More information

GIS Data Conversion: Strategies, Techniques, and Management

GIS Data Conversion: Strategies, Techniques, and Management GIS Data Conversion: Strategies, Techniques, and Management Pat Hohl, Editor SUB G6ttlngen 208 494219 98 A11838 ONWORD P R E S S V Contents SECTION 1: Introduction 1 Introduction and Overview 3 Ensuring

More information

AUTOMATED BUILDING DETECTION FROM HIGH-RESOLUTION SATELLITE IMAGE FOR UPDATING GIS BUILDING INVENTORY DATA

AUTOMATED BUILDING DETECTION FROM HIGH-RESOLUTION SATELLITE IMAGE FOR UPDATING GIS BUILDING INVENTORY DATA 13th World Conference on Earthquake Engineering Vancouver, B.C., Canada August 1-6, 2004 Paper No. 678 AUTOMATED BUILDING DETECTION FROM HIGH-RESOLUTION SATELLITE IMAGE FOR UPDATING GIS BUILDING INVENTORY

More information

Real GDP Growth to Clock 6.75 Percent this Fiscal. Economic Survey Predicts Percent Growth in

Real GDP Growth to Clock 6.75 Percent this Fiscal. Economic Survey Predicts Percent Growth in ETEN Enlightens-Daily current capsules (Prelims Prominence) 30 th Jan 2018 Economic Survey 2017-18 Real GDP Growth to Clock 6.75 Percent this Fiscal Economic Survey Predicts 7-7.5 Percent Growth in 2018-19

More information