Forecasting Wildfire Behavior: Earth Observation and GIS
Advances in GIS, remote sensing, and machine learning are leveraging land and weather data to improve fire prediction models.
Reliability of Machine Learning Maps
Academics are increasingly adopting machine learning maps to better understand what can happen for a range of environmental events.
Run AI-Mapping in QGIS over high-resolution satellite imagery
Mapflow is a QGIS plugin that lets users extract real-world objects from satellite imagery.
Mapping Schools with Artificial Intelligence
Researchers are using AI to map schools in countries where many schools are undocumented so as to connect children with schools.
Machine Learning and Object Detection in Spatial Analysis
Picterra offers a relatively easy to use interface that allows users train AI on satellite and aerial imagery to detect features.
Open Repository for Geospatial Training Data Released
Radiant Earth has launched Radiant MLHub, a cloud-based open library for training geospatial data used by machine learning algorithms.
Using Machine Learning and Surface Deformation Data to Predict Earthquakes
There is no easy solution for earthquake prediction, but machine learning in particular has made forecasting far better.
Optimizing Traffic with Autonomous Vehicles and Machine Learning
Improving traffic can be achieved by incorporating geospatial technology in autonomous cars, such as sensors and external information.
When AI Goes Wrong in Spatial Reasoning
Machines can learn ‘wrong’ or biased information, creating large problems and poor conclusions when it comes to spatial data.
Using Machine Learning and Satellite Imagery for Street Address Generation
Facebook and MIT Labs researchers have developed a method employing machine learning and satellite images to create street addresses in regions lacking unique building addresses.
Mapping Tree Species in the Amazon Using Satellite Imagery and Machine Learning
Researchers have developed a method of modeling tree species distribution in Peruvian lowland Amazonia using satellite imagery and machine learning techniques.
Detecting Storm Intensity from Satellite Imagery Using Machine Learning
The Deep Learning-based Hurricane Intensity Estimator is an experimental portal that uses machine learning techniques to analyze spatial patterns in infrared satellite imagery in order to predict tropical cyclone intensity.