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This research paper examines the potential of using publicly accessible property data, street-level imagery, and machine learning to overcome data fragmentation and gaps that hinder systematic social housing retrofit planning in the UK.

This research paper evaluates the effectiveness of various machine learning and deep learning models in predicting agricultural drought indicators, specifically soil moisture and the Palmer drought severity index (PDSI), across Sweden.

This research paper describes the application of convolutional neural network (CNN)-based machine learning models to detect and monitor retrogressive thaw slumps (RTS) in high latitude northern permafrost using Sentinel-2 satellite data.

The World Resources Institute presents a random forest model that captures 86% of future conflicts (defined as 10+ fatalities per year), though with a 50% false positive rate. While water-related variables correlate with conflict, they are not empirically significant for model decisions unless the conflict definition is modified. A web-based tool is provided to visualize these forecasts and vulnerabilities.