In this paper we develop a deforestation detection pipeline that incorporates optical and Synthetic Aperture Radar (SAR) data. A crucial component of the pipeline is the construction of anomaly maps of the optical data, which is done using the residual space of a discrete Karhunen-Loève (KL) expansion. Anomalies are quantified using a concentration bound on the distribution of the residual components for the nominal state of the forest. This bound does not require prior knowledge on the distribution of the data. This is in contrast to statistical parametric methods that assume knowledge of the data distribution, an impractical assumption that is especially infeasible for high dimensional data such as ours. Once the optical anomaly maps are computed they are combined with SAR data, and the state of the forest is classified by using a Hidden Markov Model (HMM). We test our approach with Sentinel-1 (SAR) and Sentinel-2 (Optical) data on a $92.19\,km \times 91.80\,km$ region in the Amazon forest. The results show that both the hybrid optical-radar and optical only methods achieve high accuracy that is superior to the recent state-of-the-art hybrid method. Moreover, the hybrid method is significantly more robust in the case of sparse optical data that are common in highly cloudy regions.

As urbanization and climate change progress, urban heat island effects are becoming more frequent and severe. To formulate effective mitigation plans, cities require detailed air temperature data. However, predictive analytics methods based on conventional machine learning models and limited data infrastructure often provide inaccurate predictions, especially in underserved areas. In this context, geospatial foundation models trained on unstructured global data demonstrate strong generalization and require minimal fine-tuning, offering an alternative for predictions where traditional approaches are limited. This study fine-tunes a geospatial foundation model to predict urban land surface temperatures under future climate scenarios and explores its response to land cover changes using simulated vegetation strategies. The fine-tuned model achieved pixel-wise downscaling errors below 1.74 °C and aligned with ground truth patterns, demonstrating an extrapolation capacity up to 3.62 °C.

Land cover and land use (LULC) changes are key applications of satellite imagery, and they have critical roles in resource management, urbanization, protection of soils and the environment, and enhancing sustainable development. The literature has heavily utilized multispectral spatiotemporal satellite data alongside advanced machine learning algorithms to monitor and predict LULC changes. This study analyzes and compares LULC changes across various governorates (provinces) of the Sultanate of Oman from 2016 to 2021 using annual time steps. For the chosen region, multispectral spatiotemporal data were acquired from the open-source Sentinel-2 satellite dataset. Supervised machine learning algorithms were used to train and classify different land covers, such as water bodies, crops, urban, etc. The constructed model was subsequently applied within the study region, allowing for an effective comparative evaluation of LULC changes within the given timeframe.

The task of the contest is to create high-resolution (1m / pixel) land cover change maps of a study area in Maryland, USA, given multi-resolution imagery and label data. We study several baseline models for this task and discuss directions for further research.

US: The US Geological Survey (USGS) has selected Hexagon US Federal to upgrade the machine learning-based Land Cover Mapping (LCM) tool. Through more than a decade of success using the LCM tool powered by Hexagon's ERDAS IMAGINE, the USGS has continued to achieve its mission of supplying timely, relevant, and useful information about the earth and the changes experienced by the national land cover. Since 2005, the LCM tool has generated USGS's National Land Cover Database that collects public domain information on the 3.8 million miles of land cover in the United States and Puerto Rico. This information provides complete, current, and consistent information critical to government managers and officials that seek to understand how land cover changes over time. "We're proud to be part of this worthwhile mission and provide critical tools and support to USGS," said Brad Ward, Hexagon US Federal senior vice president for geospatial solutions.