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James Sill, Senior Solution Engineer, Esri, Boulder, Co
Jacob Czawlytko, Chesapeake Conservancy, Senior Geospatial Analyst
Kumar Mainali, Chesapeake Conservancy, Geospatial Data Scientist
Automated Feature Extraction In ArcGIS
Agenda
• What is feature extraction in ArcGIS
• Methods for solving the problem
- Unsupervised vs. Supervised
- Collecting and managing training samples in ArcGIS Pro, Enterprise and Online
- Deep Learning in ArcGIS for Feature Extraction
• Real Life Examples
- Supervised classification of Landcover with Raster Analytics
- Integrating external deep learning frameworks into ArcGIS
- Chesapeake Conservancy Landcover Classification
Tools for Feature Extraction from Imagery In ArcGIS
ArcGIS
Classification
Clustering
Prediction
Deep Learning
Machine Learning Tools in ArcGIS
• Pixel & Object Based
• Image Segmentation
• Maximum Likelihood
• Random Trees
• Support Vector Machine
• Empirical Bayesian Kriging
• Areal Interpolation
• EBK Regression Prediction
• Ordinary Least Squares Regression and Exploratory Regression
• Geographically Weighted Regression
Classification
PredictionClustering
• Spatially Constrained Multivariate Clustering
• Multivariate Clustering
• Density-based Clustering
• Hot Spot Analysis
• Cluster and Outlier Analysis
• Space Time Pattern Mining
• Generate training samples
• Detect objects
• Classify pixels
• End to End support
• Apply at Scale over large collections
Deep Learning
Built – in tools for Feature Extraction
in ArcGIS Pro
Pixel or Object Based
Unsupervised
Choose Image
ISO Clusters
Assign Classes
Clean Up
Merge Classes
GOAL
Unsupervised Method:
• ISO Clustering:
- Based on K-means nearest neighbor algorithm
- Quick, least cost classification method
- Good for instances where there is a low familiarity with
composition of area of interest
- Generally low overhead
- Advantages where a lack of resources to create training
samples
- Use cases where unsupervised methods excel…
- Flood extent mapping – ISO/Kmeans clustering to delineate
water from non-water
- Presence absence of vegetation in post wild fire burn
zones
- Distinct differences in spectral composition within your
AOI
Pixel and Object Based
Supervised
Choose ImageTraining Samples
Maximum Likelihood
Accuracy
Classify
GOAL
Forest Based Classification
Support Vector Machine
Supervised
Choose Image
Segmentation
Training Samples
Accuracy
Support Vector
Machine
GOAL
Classify
Object Classification
Supervised Feature Extraction and Pixel Level Classification
• Pixel and Object Based Classification/Identification
- Pixels are classified or objects identified through supervised algorithms
- Support Vector Machine
- Forest Based Classification
- Maximum Likelihood
- Requires that the user collects training samples
- Instances where supervised classification excels:
- Ability and time to create training samples
- Example Use cases where Supervised Classification Excels
- Multiclass landcover classification and feature identification
- Impervious/non-impervious surface mapping
- Static data products that produced in a non – time sensitive environment
• Support Vector Machine classification of
Sage Grouse Habitat in Southwest
Colorado (ArcGIS Pro, Image Server,
Raster Analytics)
Demo: Supervised
Machine Learning for
Feature Extraction in
ArcGIS Pro
ImageryAccess
Imagery Prep
Creating Training
data
Inference
Distributed Processing
Feedback Loop
TakeAction
Feature Extraction and Machine Learning with ArcGIS: End to End Cycle
Training Derive Products
Generate Training
Samples
Imagery
Training ToolsTraining sites
ArcGIS – Machine Learning Workflow
ArcGIS Professional Image (Data) Scientist
Inferencing Tools
Inference results
Pixel & Segment Based
Training Engine
Deep Learning BasedDeep Learning
Machine Learning
Feedback Loop
MachineLearning
DeepLearning
Detailed Workflow
Model
Definition
ArcGIS User
Input Images
Machine Learning:- Support Vector Machine- Random ForestDeep Learning:- TensorFlow*- CNTK*- PyTorch*- Custom*- + External via Python
*Requires framework installed
Pixel & Segment Based:- Maximum Likelihood- Support Vector Machine- Random ForestDeep Learning Based:- TensorFlow*- CNTK*- PyTorch*- Custom*
*Run External to ArcGIS
End-to-end from raw imagery to structured information products
Labelling Data
PrepTrain/Fit
Model
Prediction AnalysisField
Mobility, Monitoring
Feature Extraction Workflow in ArcGIS
Image
Service/
Mosaic
Dataset
Imagery
Management
Deep Learning
Key imagery tasks for deep learning
Impervious Surface
Classification
Agricultural Crop
Detection
Building Footprint
Extraction
Damaged House
Classification
Pixel Classification Object Detection Instance Segmentation Image Classification
Where Deep Learning Excels
• Resources to create and maintain robust training datasets
• Access to GPU’s to train and apply model… ☺
• Well defined problem and general knowledge of an area
• Imagery collected under consistent conditions with minimal
variations in quality
• Large scale monitoring problems
- There is a need to repeatedly measure the activity, composition
and change in a particular area over the course of time
- Use cases
- Monitoring and identifying changes in landcover over time
- Object detection- i.e.. Counting specified objects
- Classification of detected objects – i.e.. Damaged or undamaged
houses
From Change Detection to Monitoring…
Deep Learning with Imagery in ArcGIS ArcGIS supports end-to-end deep learning workflows
• Tools for:
• Labeling training samples
• Preparing data to train models
• Training Models
• Running Inferencing
• Supports all 4 imagery deep learning categories
• Supports image space, leverage GPU
• Clients
• ArcGIS Pro
• Map Viewer
• NotebooksPart of ArcGIS Image Analyst
Run distributed on ArcGIS Image Server
• Managing Training Data and
Applying a Deep Learning Model
in ArcGIS Pro for Object
Detection and Monitoring
Demo: Deep
Learning
Conservation Innovation Center
The CIC was created by Chesapeake Conservancy to
help shape proactive responses for one of the world’s
largest environmental efforts—restoring the
Chesapeake Bay.
Since then, the CIC has continued to pioneer high-
resolution GIS mapping that provides new
perspectives about the state of landscapes and
waterways. This information is used to identify
specific project-level priorities that can maximize
conservation outcomes.
The Problem
• Poultry houses are important to Chesapeake
Bay TMDL
- mapping and accounting agricultural BMPs
• USDA data is restricted
- Unknown total number in Chesapeake Bay
Watershed
• USGS data is new but limited to Delmarva
• Very little available geospatial data on poultry
houses
• 5,747 poultry houses in the Delmarva peninsula
identified using 2016 and 2017 USDA NAIP by
USGS
• “Very little” available geospatial data on
poultry houses
What’s the plan?
• Utilize ArcGIS tools and existing datasets to training data(USGS poultry houses)
• Export Training Data For Deep Learning in ArcGIS Pro
• Detect Objects Using Computer Vision
• Run Detect Objects Using Deep Learning (ArcGIS Pro)
• Compare results
Data and Models
• Input layers: red, NIR, thermal, ndsm, NDVI
• Main model: computer vision
- Scale Invariant Feature Transform (SIFT)
- Gray Level Co-Occurrence Matrices (GLCM)
• Other considerations:
- Traditional machine learning models with features extracted manually
• Questions to test:
- How much benefit is there of using computer vision
- do traditional machine learning models perform comparable with extracted features?
Next steps
• Pass data to BMP team to integrate dataset into BMP analysis
• Accuracy assessment
• Use same methods to identify novel classes
Links and Contact
chesapeakeconservancy.org/conservation-innovation-center
sciencebase.gov/catalog/item/5e0a3fcde4b0b207aa0d794e
Questions?
You can find me at the Civilian – Sciences Area of the Expo hall
James Sill
Thank You!!!
Presenter(s)
Demo Title