Topic
Artificial intelligence, machine learning, big data and cloud platforms applied to geospatial problems.
Drone technology is evolving beyond aerial photography. Learn how GeoAI, GIS, and spatial analytics are helping modern drone professionals transform imagery into actionable intelligence across agriculture, infrastructure, and environmental management.
AI and ML are revolutionizing forest conservation by enabling real time monitoring of deforestation, wildlife protection and climate resilience. From satellite imagery and smart sensors to digital twins and predictive analytics, these technologies help conservationists make data driven decisions.
GeoAI is helping African cities tackle flooding, informal settlements, traffic congestion, and climate risks through intelligent geospatial analysis and predictive decision-making.
AI and Machine Learning are revolutionizing forest conservation by combining satellite imagery, remote sensing, and GIS to detect illegal logging, predict deforestation hotspots, and support rapid environmental response efforts.
The integration of AI and Machine Learning with GIS is driving a paradigm shift. From automated land use classification via CNNs to real-time tracking of Amazonian deforestation and predictive disaster mapping, intelligent GIS is evolving into our most proactive tool for global sustainability.
Explore how Google Earth Engine and Google Cloud work together to process massive geospatial datasets for environmental monitoring and research.
Discover how GIS and machine learning are being combined to analyze crime patterns, predict high-risk locations, and assist law enforcement decision-making.
A gradient boosting model using free elevation, CORINE and OpenStreetMap data maps old-growth habitat across small private woodlands in Lower Saxony.
Source: Annals of Forest Science
Trimble's WorksManager Drone processes DJI and Wingtra flight data in the cloud and overlays it with designs and machine data, due January 2027.
Source: TrimbleA Mamba-based model using Sentinel-2 and crop growth parameters estimates seed maize yields in Gansu with an R2 of 0.78, ahead of Transformer, LSTM and XGBoost.
Source: Bioengineer.org
Melbourne hosts the 10th Digital Earth Summit and Locate26 from 24 to 26 November, with streams on GeoAI, 3D cadastre, digital twins and Earth observation.
Source: Spatial SourceAfSA is taking applications until 30 November for a five-day EO, GIS and AI course in Rabat in January 2027, covering agriculture, land use and water security.
Source: Tech Review Africa
CoStar Group has agreed to buy Vexcel, maker of UltraCam cameras and operator of a 45-country aerial imagery programme, with closing expected in Q1 2027.
Source: CoStar Group
The open 10-metre ForTy map separates primary forest, plantations and tree crops worldwide, with a 2017–2025 time series on Google Earth Engine.
Source: Google DeepMind
A physics-guided deep learning model treats satellite viewing-angle effects as crop-structure signals, reaching R² of 0.69 for soybean yields across 613 US counties.
Source: Newswise
An EU Results Pack profiles eight Horizon Europe projects on on-board AI, edge computing, data platforms and ground segments for commercial Earth observation.
Source: CORDISA new study fuses satellite environmental data with explainable machine learning to flag high cardiovascular risk areas, reaching 95% accuracy on Ohio data.
Source: Discover Artificial Intelligence (Springer)