The Changing Landscape of Land Administration

Transcription

1 The Changing Landscape of Land Administration B r e n t J o n e s P E, PLS E s r i

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3 World s Largest Media Company No Journalists No Content Producers No Photographers

4 World s Largest Hospitality Company No Rooms No Receptionists No Free Breakfast

5 World s Largest Database - Blockchain No Operating System No Infrastructure No Database Admin

6 World s Largest Currency No Vault No Tellers No Drive-up Window

7 World s Largest Transportation Company No Cars No Manufacturing No Repair Facilities

8 Industrial Revolutions First ~ Second ~ Third ~ Forth ~ ?

9 *aas

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13 willrobotstakemyjob.com/

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16 Land Administration Tenure Value Land Use Land Development Land Market Cadastre Valuation Permitting Planning Land Market Registration Taxation Land Consolidation Acquisition Trading Titling Expropriation Surveying Addressing Land Applications Land Services Land Information Data

17 Artificial Intelligence Video game behavioral AI Natural Language Processing Computer Vision Theano IBM Watson Machine Learning CNTK Deep Learning Keras TensorFlow scikit-learn Robotics

18 It s applied everywhere.. Autonomous Cars Sentiment Analysis Chatbots War Robots Advanced Video Analytics Crime Prediction Predictive Maintenance Advanced Feature Extraction Cancer Detection Facial Recognition Personalized Marketing Advanced Feature Extraction Stock Market Prediction

19 Machine Learning Cloud ML On-premise ML Enhanced Products New Products

20 The Machine Learning Race Top Active Companies & Categories

21 GIS Machine Learning Tools Classification Clustering GIS Prediction

22 Prediction Using the known to estimate the unknown Use Case: Accurately predict impacts of climate change on local temperature using global climate model data In ArcGIS: Empirical Bayesian Kriging, Areal Interpolation, EBK Regression Prediction, Ordinary Least Squares Regression and Exploratory Regression, Geographically Weighted Regression

23 Clustering The grouping of observations based on similarities of values or locations Use Case: Given the nearly 50,000 reports of traffic between 5pm and 6pm in Los Angeles (from Traffic Alerts by Waze), where are traffic zones that can be used to elicit feedback from current drivers in the area? In ArcGIS: Spatially Constrained Multivariate Clustering, Multivariate Clustering, Density-based Clustering, Image Segmentation, Hot Spot Analysis, Cluster and Outlier Analysis, Space Time Pattern Mining

24 Classification The process of deciding to which category an object should be assigned based on a training dataset Use Case: Classify impervious surfaces to help effectively prepare for storm and flood events based on the latest high-resolution imagery In ArcGIS: Maximum Likelihood Classification, Random Trees, Support Vector Machine

25 Integration with External Frameworks GIS

26 Advanced Object Detection from Imagery Discover Deep Hidden Insights from Imagery Data

27 Smart Road Snapping

28 Road Detection from Satellite Imagery

29 Real-Time Activity Detection Using Deep Learning with ArcGIS API for Python and Operations Dashboard

30 Automated Mapping with Lidar Courtesy: Esri Canada

31 Combining Capabilities Courtesy: Esri Canada

32 Vertical and Horizontal Equity

33 High Resolution Land Cover Using Deep Learning to achieve 1-meter resolution land cover at scale

34 Manually created a high-resolution land cover map for precision conservation of the Chesapeake watershed 100k mi 2 Area of watershed to map 2TB File size of imagery to classify 18 months Time to create map By the time the land cover map was completed in December 2016, it was already out of date, and an update would be time-intensive and costly.

35 Land Classification Model Labeled Training Images Chesapeake Conservancy Dataset Convolutional Network Architecture 23 layer U-Net Working Platform: GeoAI Virtual Machine Dataset: 120k mi 2 of imagery at 1-meter resolution, split in half geographically into train and test sets Test Images Land Classification Model Land Cover Map Algorithm Results 91% Average land classification accuracy 16x Faster than Chesapeake Conservancy s previous methods

36 High Resolution Land Classification Using CNTK Convolutional Neural Networks - Microsoft Cognitive Toolkit (

37 ETA Prediction Using Deep Learning with Network Analyst to Predict ETA from Downtown San Francisco to different areas under 25 Minutes

38 Nigeria - Polio

39 3D Cadastre

40 So what do we do now? How do we stay relevant?

41 Brent s Seven Rules for Success

42 1. Think Big and Spatial

43 2. Use Your Tools

44 3. Stay Current

45 4. Create New Services

46 5. Be Free or Affordable

47 6. Move Quickly & Evolve It is not the strongest of the species that survives, nor the most intelligent that survives. It is the one that is most adaptable to change.

48 7. Get Started Now

49 GIS Brent Jones, PE, PLS

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