GeoAI: Deep Learning meets ArcGIS - Harris Geospatial · NLP Robotics Machine Learning 1. Training...
Transcript of GeoAI: Deep Learning meets ArcGIS - Harris Geospatial · NLP Robotics Machine Learning 1. Training...
GeoAI: Deep Learning meets ArcGIS
Omar Maher
Practice Lead, Advanced Analytics. ESRI
1. AI > ML > DL
Reasoning Knowledge Representation
Perception
RoboticsNLP Machine Learning
features Labels1. Training
2. Predicting
Supervised Learning
Unsupervised LearningReinforcement Learning
Deep Supervised Learning
Artificial Intelligence Machine Learning Deep Learning
Dog
Machine Learning
Deep Learning
Artificial Intelligence
CNTK TensorFlowTheano
Natural Language Processing
Video game behavioral AI
Robotics
Keras
IBM Watson
scikit-learn
Computer Vision
ArcGIS has Machine Learning Tools
ArcGIS
Classification
Clustering
Prediction
Machine Learning Tools in ArcGIS
•Maximum Likelihood Classification•Random Trees• Support Vector Machine
Clustering
• Empirical Bayesian Kriging•Areal Interpolation• EBK Regression Prediction•Ordinary Least Squares
Regression and Exploratory Regression•Geographically Weighted
Regression• Spatially Constrained Multivariate Clustering•Multivariate Clustering•Density-based Clustering• Image Segmentation•Hot Spot Analysis•Cluster and Outlier Analysis• Space Time Pattern Mining
Classification Prediction
Integration with External Frameworks
ArcGIS
GeoAI Sample Use-Cases (Videos)
Accidents Probability Prediction
High Resolution Land CoverObject Detection from Imagery Object Detection from Videos
Object Detection from Imagery Building Detection
Advanced Object Detection from Imagery Discover Deep Hidden Insights from Imagery Data
CAFO Site Detection using Deep Learning + ArcGIS Pro
1. Sample Training Data (Feature Layer of 700 CAFO Site)
3. Export Training Data 4. Train CNN
5. Detect Objects6. Call the model directly from Pro.. ..Via Python Raster Function
2. Add Imagery Source (NAIP)
Imagery Tools for Deep Learning
Bounding Boxes
Labelled Pixels+
Export Training Data For Deep LearningAvailable with Spatial Analyst / Image Analyst license
GIS Feature Class Imagery Data
Parking Utilization per Timeusing Fast RCNNs and Operations Dashboard
Object Detection from Satellite Imageryusing Deep Learning with ArcGIS Pro
Electric Substation Detection
AI with ArcGIS: End to End Cycle
4. ArcGIS Field Apps like Workforce + Collector and Survery123 could be used to plan inspections for detected sites. Operations Dashboard could be used to monitor execution of assigned tasks in Real-TimeInspections Results could then be analyzed in ArcGIS
1. ArcGIS “Export Training Data For Deep Learning” GP tool used to Prepare the a labelled training data set from feature class
2. CNTK or TensorFlow used to train a CNN to detect objects of interest using the labelled training data set
3. ArcGIS Imagery tools used for imagery management and analytics. A Raster Function is used to call the trained CNN and generate the results directly at Pro, allowing for further vector and raster analytics
Unassessed Pools DetectionIntegrating Deep Learning with ArcGIS
Road Detection from Satellite Imageryusing UNet with ArcGIS Pro
High Resolution Land CoverUsing Deep Learning to achieve 1-meter resolution land cover at scale
manually created a high-resolution land cover map for precision conservation of the Chesapeake watershed
100kmi2Area of watershed to map
2TBFile size of imagery to classify
18monthsTime 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.
Land Classification Model
Convolutional
Network
Architecture
23 layer U-Net
Test ImagesLabeled
Training Images
Chesapeake
Conservancy
Dataset
Land
Classification
Model
Land Cover Map
Working Platform: GeoAI Virtual Machine
Dataset: 120k mi2 of imagery at 1-meter resolution,
split in half geographically into train and test sets
Algorithm Results
91%Average land
classification accuracy
16xFaster than Chesapeake
Conservancy’s previous
methods
High Resolution Land Classificationusing CNTK Convolutional Neural Networks
Real-Time Activity Detectionusing Deep Learning with ArcGIS API for Python
Blight/Graffiti/Cracks Detection from Street view Imagesusing Azure Custom Vision Service with PhotoSurvey
Buildings DetectionDetect Buildings, Roof Type, Height, and More!
Workflow
Labelled Lidar or Aerial Imagery
Export Training Data for Deep Learning
tool
Train Model using Mask RCNN
Output: Detected Buildings
+ Roof Type, Footage, Height and # Floors (if we have
Lidar)
Raster Function Runs Model w/ New
Imagery
Buildings Converted to 3D
Regularize Building Footprint (GP)
1. Labelled Lidar or Aerial Imagery 2. Detected Buildings
3. 3D Buildings w/ Roof Types
Automated Buildings Detection using Deep Learning
Combining the AI power of Microsoft with the geospatial analytics of Esri.
Pre-Configured environments in the cloud for GeoSpatialData Science & AI Modelling, Development & Deployment.
https://azure.microsoft.com/en-us/blog/microsoft-and-esri-launch-geospatial-ai-on-azure/
GeoAI Virtual Machine
ArcGIS Pro
Accidents PredictionPredict Accident Probability per Segment per Hour
What would Cause an Accident?
Temperature
Sun, Mon, Fri..
Visibility
High/Low
Wind Speed
Fast, Slow..
Snow Depth
High/Low
Time of the Day
12:45, 23:00
Day of the Week
Sun, Mon, Fri..Month
Feb, Dec..
Road Alignment
Straight / Curved
Proximity to
Intersections
Speed Limit
120 km/h
Sun Direction
East, West
Daily Traffic
AADT
Proximity to
Billboards
…
7 Years of Data400,000 Accidents500,000 Segments
10s of Variables
Road Width
20-30 M
Impossible to Manually Analyze
Train a Machine to do?
Accidents Probability Prediction using Scikit Learn XGBoost with ArcGIS Pro