The following presentation was delivered by Robert Morrison, Principal Consultant at Esri Ireland, at the 2019 NICS ICT Conference in October 2019. The presentation focuses on taking a geographic approach to machine learning to help you "see what other's can't". Imagery and remotely sensed data is a valuable resource for many organisations who have made substantial investment obtaining the data. The field of Machine Learning is both broad and deep and is constantly evolving. Using ArcGIS and Machine Learning allows organisations to derive valuable new content. ArcGIS is an open, interoperable platform that allows for the integration of complementary methods and techniques that empower ArcGIS users to solve complex, real-world problems in a fundamentally spatial way. Learn how by combining powerful built-in Image analysis tools with any machine learning package users can benefit from the spatial validation, geo-enrichment and visualisation. See how this Machine Learning is being applied in real world use-cases from marine farming and crime analysis to agriculture and sustainability.

1. Remote Sensing Imagery and ArtificiaI Intelligence
Robert Morrison
Principal Consultant at Esri Ireland

2. Remote Sensing Imagery and Artificial Intelligence
Radio Frequency Tracking
3D Buildings
Telco
Modeling
Vegetation Visualization
UAV Data
Capture
Lidar-Based Topo Map
Elevation and
Feature Extraction
Wildfire Imagery
Feature Extraction
Hydrology Analysis
Hurricane Damage
Florida
Wildfire
Aerial Imagery

3. Neural Networks
TensorFlow
CNTK
Natural Language
Processing
Cognitive
Computing
GeoAI
Computer Vision
Object
Detection
Support Vector Machines
Object Tracking
Theano
scikit-learn
T-SNE
Random Forest
Caffe
Machine
Learning
Deep
Learning
Artificial Intelligence

4. Computer does a task with
some level of human
intelligence
Techniques that learn from
data
Loosely resembles the
human brain
Machine
Learning
Deep
Learning
Artificial Intelligence

5. Image Classification Object Detection Semantic Segmentation Instance Segmentation
Image Use Cases

6. GIS Includes Machine LearningTools
GIS
Classification
Clustering
Prediction
Deep Learning

7. Precision agriculture Asset management
Suitability analysis Change detection
Imagery contains ‘data’ - Machine learning extracts ‘information’
Fleet management
Agricultural yield
Target identification
Machine Learning and Imagery
Machine
Learning
GIS
Derived structured
information products

8. Imagery
Access
Imagery
Prep
Creating
Training
data
Inference
Distributed
Processing
Feedback
Loop
Take
Action
AI with GIS: End to End Cycle
Training Derive
Products

9. Case Study
Palm Tree Health

10. - Tonga is a collection of 170 islands in the South Pacific
- Economy is heavily dependent on coconuts
- Efficient management of coconut plantations is vital
What if, instead of manually visiting every tree and
inspecting, we used imagery to automate the process?
Coconut Palm Health in the Kingdom of Tonga

11. Kolovai Palms

12. Train the data

13. Export the training data

14. Execute the deep learning model

15. Execute the deep learning model

16. Assess individual palm health

17. Derived Information Product
1. Create a point for each palm.
2. Buffer each point by 3m.
3. Intersect with TGI imagery.
4. Calculate zonal statistics.

18. Deep Learning Examples
Impervious Mapping Change Detection
Flood Planning
Riparian CorridorsLandcover Classification
and more…
Planning Compliance
Environment Compliance
Emergency Management

19. Robert Morrison
rmorrison@esri-Ireland.ie