This article presents a curated list of eight widely used remote sensing object detection datasets covering indoor scenes, landslides, drone imagery, crop diseases, safety vests, human fractures, urban issues, and plant diseases, each with size estimates and direct download links for researchers.
This paper presents the Temporal‑Spectral‑Spatial Unified Network (TSSUN), a flexible deep‑learning architecture that simultaneously handles semantic segmentation, semantic change detection, and binary change detection across heterogeneous remote‑sensing inputs, achieving state‑of‑the‑art performance without task‑specific retraining.
The NK‑Remote repository introduces LSKNet and Strip R‑CNN, two lightweight yet powerful models for remote‑sensing object detection that dynamically adjust receptive fields and combine square‑and‑strip convolutions, achieving state‑of‑the‑art performance on benchmarks such as DOTA, FAIR1M, HRSC2016, and DIOR.
The talk presents a comprehensive overview of high‑resolution hyperspectral intelligent fusion imaging, covering its background, three major research contributions—including tensor‑coupled imaging mechanisms, structured low‑rank tensor models, and model‑guided low‑rank fusion methods—and outlines future research challenges and directions.
Ant Group introduced SkySense, a 2.06‑billion‑parameter multimodal remote‑sensing foundation model that outperformed 18 international rivals across 17 benchmark tasks, was accepted to CVPR 2024, and aims to support applications such as agriculture, urban planning, and disaster response.
This article provides a comprehensive overview of UAV‑based oblique photogrammetry for real‑world 3D modeling, detailing background, step‑by‑step field capture, control‑point handling, aerial triangulation, model reconstruction, internal processing, and emerging trends in smart‑city applications.
A nationwide “Code to Space” competition, backed by Huawei Cloud, Beijing University of Posts and Telecommunications, and others, invited student teams to develop cloud‑native, AI‑driven satellite applications, resulting in award‑winning projects like CoStar that dynamically split neural‑network models between edge and cloud, boosting efficiency and reducing bandwidth.
The article explains how satellite images suffer from limited spatial resolution, why the ordering of multi‑temporal frames is irrelevant, and how the PIUnet model introduces permutation‑invariant equivariant layers to achieve state‑of‑the‑art super‑resolution efficiently, winning the AI4EO challenge.
This article presents a Python implementation for calculating remote sensing ecological indices—including wetness, greenness, temperature, dryness, and a combined ecological index—using Landsat imagery, detailing the code, usage instructions, and methods for reading data, computing indices, normalizing results, and saving output images.
