Balancing nitrogen use is critical for maximizing crop yield while minimizing environmental and economic costs. A new approach integrates drone-based multispectral imaging with machine learning to ...
Researchers at UCLA's Institute of the Environment and Sustainability have developed the most high-resolution statewide maps of California's kelp forests to date, giving researchers, conservationists ...
Researchers from UC Berkeley, Yale, Stanford’s Global Policy Laboratory, and NBER developed a deep learning method to predict ...
Drone pilots earn USDC on Base blockchain to map Earth for AI world models. Spexi's LayerDrone network delivers imagery 900x ...
The organization folds the Taylor Geospatial Institute and Taylor Geospatial Engine together to spur new geospatial artificial intelligence tools.
Accurate land use/land cover (LULC) classification remains a persistent challenge in rapidly urbanising regions especially, in the Global South, where cloud cover, seasonal variability, and limited ...
TorchGeo is a Python package for integrating geospatial data into the PyTorch deep learning ecosystem, making it easy for machine learning and remote sensing experts to use geospatial data in their ...
Transylvania was the setting for dramatic social transformations during the Bronze Age around 4000 years ago. A key question remains: how did inequality become a common part of European societies?
Urban gender-based violence (GBV) is not randomly distributed across city space, and new research shows that advanced geospatial machine learning can identify where risks concentrate with striking ...
Abstract: The way we handle spatial data has changed significantly as a result of the integration of machine learning with geospatial data. Machine Learning techniques increase the accuracy, efficacy, ...
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