FLORO is a multimodal geospatial foundation model that learns transferable remote sensing representations from a small, diverse corpus, achieving strong performance across various sensor types and res…

The paper advocates for a paradigm shift toward joint Spatial Representation Learning (SRL) that unifies raster imagery and structured vector data into a single embedding space for developing more sem…

The paper introduces CAFOSat, a large-scale, strongly annotated, and infrastructure-aware dataset designed to improve the accuracy of mapping Concentrated Animal Feeding Operations (CAFOs) from high-r…

This paper introduces MonoIR-RS, a large-scale infrared remote-sensing vision-language dataset and benchmark for understanding infrared imagery.

This paper introduces a novel cloud-removal framework using Denoising Diffusion Probabilistic Models and a Masked Diffusion Transformer to generate cloud-free multispectral flood imagery, significantl…

The paper introduces a knowledge distillation framework to adapt a dead tree detection model trained on one geographical area (Finland) to multiple diverse forest types (Poland, Germany, Estonia), ach…

DarkVesselNet is a novel multi-modal deep learning framework that fuses SAR, optical, and AIS data to accurately detect vessels that do not report their presence via Automatic Identification System (A…

This paper introduces IR275K, a curated benchmark for multi-frame super-resolution in infrared remote sensing, and evaluates CGMamba, a lightweight state-space model, achieving state-of-the-art perfor…

This paper proposes a new sampling strategy for Exploratory Landscape Analysis using random linear embeddings to improve the robustness of landscape descriptors when budgets are limited.

LALE introduces a novel lightweight architecture that efficiently combines local convolutional features and global transformer context for land-cover segmentation, achieving superior efficiency and pe…

The paper introduces a novel third-order, rotation-invariant spherical bispectrum for watermarking panoramic images, enabling reliable watermark embedding and extraction under arbitrary 3D rotations.

This paper systematically explores the convex polygon reconstruction problem with specified sets of features, contributing new testing algorithms and hardness results.

Places in the Wild introduces a massive, high-resolution RAW photograph dataset of 67,574 images captured in situ across 810 locations, providing unprecedented detail for ecologically valid vision res…

This paper demonstrates that standard image classifiers can be interpreted as multiple-instance learning models, allowing for the recovery of spatial class evidence from image-level logits.

This paper proposes a framework using building morphology from satellite imagery for fine-scale urban affluence mapping in Indian cities.

The paper proposes an attention-enhanced deep learning framework using EfficientNet and CBAM to achieve high accuracy (93.3%) in classifying peach leaf damage, demonstrating improved robustness under…

The paper describes how to compute singular value soft-thresholding using matrix polar decomposition for faster GPU processing.

This paper proposes a multi-modal reconstruction framework using outdated DEMs as geometric priors for image-based 3D terrain mapping in wildfire-prone regions, improving accuracy and efficiency.