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Found 4 papers, 4 papers with code
RS-Mamba for Large Remote Sensing Image Dense Prediction
RSM is specifically designed to capture the global context of remote sensing images with linear complexity, facilitating the effective processing of large VHR images.
Ranked #1 on
Road Segmentation
on Massachusetts Roads Dataset
(F1 metric)
Building change detection for remote sensing images
Change Detection
+1
LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large Multimodal Language Model
Additionally, we introduce LHRS-Bench, a benchmark for thoroughly evaluating MLLMs' abilities in RS image understanding.
Exchanging Dual Encoder-Decoder: A New Strategy for Change Detection with Semantic Guidance and Spatial Localization
We build a binary change detection model based on this strategy, and then validate and compare it with 18 state-of-the-art change detection methods on six datasets in three scenarios, including intraclass change detection datasets (CDD, SYSU), single-view building change detection datasets (WHU, LEVIR-CD, LEVIR-CD+) and a multiview building change detection dataset (NJDS).
Ranked #2 on
Change Detection
on WHU Building Dataset
CMID: A Unified Self-Supervised Learning Framework for Remote Sensing Image Understanding
We argue that this learning strategy is suboptimal in the realm of RS, since the required representations for different RS downstream tasks are often varied and complex.
