The field of remote sensing and geospatial intelligence is rapidly evolving, with a focus on developing innovative methods for analyzing and interpreting satellite and aerial imagery. Recent research has explored the use of deep learning models, such as convolutional neural networks (CNNs) and transformers, for tasks like image segmentation, object detection, and change detection. These models have shown promising results in various applications, including urban planning, environmental monitoring, and disaster response. Notably, the development of geospatial foundation models (GeoFMs) has enabled more efficient and accurate analysis of large-scale geospatial data. Furthermore, researchers have investigated the use of transfer learning and domain adaptation techniques to improve the performance of these models in diverse environments and datasets. Overall, the field is moving towards more robust, scalable, and generalizable approaches for remote sensing and geospatial intelligence. Noteworthy papers include SpecAware, which proposes a novel hyperspectral spectral-content aware foundation model for unifying multi-sensor learning, and OSMGen, which introduces a generative framework for creating realistic satellite imagery from OpenStreetMap data. Additionally, the paper on GeoCrossBench highlights the importance of cross-satellite generalization for remote sensing models.
Advances in Remote Sensing and Geospatial Intelligence
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Predicting Household Water Consumption Using Satellite and Street View Images in Two Indian Cities
SpecAware: A Spectral-Content Aware Foundation Model for Unifying Multi-Sensor Learning in Hyperspectral Remote Sensing Mapping
Mask-to-Height: A YOLOv11-Based Architecture for Joint Building Instance Segmentation and Height Classification from Satellite Imagery
MapSAM2: Adapting SAM2 for Automatic Segmentation of Historical Map Images and Time Series
A Multi-tiered Human-in-the-loop Approach for Interactive School Mapping Using Earth Observation and Machine Learning
Habitat and Land Cover Change Detection in Alpine Protected Areas: A Comparison of AI Architectures
An Efficient and Generalizable Transfer Learning Method for Weather Condition Detection on Ground Terminals
Transfer Learning for Onboard Cloud Segmentation in Thermal Earth Observation: From Landsat to a CubeSat Constellation
OSMGen: Highly Controllable Satellite Image Synthesis using OpenStreetMap Data
Leveraging Compact Satellite Embeddings and Graph Neural Networks for Large-Scale Poverty Mapping
Assessing the value of Geo-Foundational Models for Flood Inundation Mapping: Benchmarking models for Sentinel-1, Sentinel-2, and Planetscope for end-users
GeoCrossBench: Cross-Band Generalization for Remote Sensing
Desert Waste Detection and Classification Using Data-Based and Model-Based Enhanced YOLOv12 DL Model
Landslide Hazard Mapping with Geospatial Foundation Models: Geographical Generalizability, Data Scarcity, and Band Adaptability