REVIEW 24 cited by
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
read the original abstract
Deep learning approaches have shown promising results in remote sensing high spatial resolution (HSR) land-cover mapping. However, urban and rural scenes can show completely different geographical landscapes, and the inadequate generalizability of these algorithms hinders city-level or national-level mapping. Most of the existing HSR land-cover datasets mainly promote the research of learning semantic representation, thereby ignoring the model transferability. In this paper, we introduce the Land-cOVEr Domain Adaptive semantic segmentation (LoveDA) dataset to advance semantic and transferable learning. The LoveDA dataset contains 5987 HSR images with 166768 annotated objects from three different cities. Compared to the existing datasets, the LoveDA dataset encompasses two domains (urban and rural), which brings considerable challenges due to the: 1) multi-scale objects; 2) complex background samples; and 3) inconsistent class distributions. The LoveDA dataset is suitable for both land-cover semantic segmentation and unsupervised domain adaptation (UDA) tasks. Accordingly, we benchmarked the LoveDA dataset on eleven semantic segmentation methods and eight UDA methods. Some exploratory studies including multi-scale architectures and strategies, additional background supervision, and pseudo-label analysis were also carried out to address these challenges. The code and data are available at https://github.com/Junjue-Wang/LoveDA.
Forward citations
Cited by 24 Pith papers
-
Rotation Equivariant Mamba for Vision Tasks
EQ-VMamba adds rotation-equivariant cross-scan and group Mamba blocks to enforce end-to-end rotation equivariance, yielding better rotation robustness, competitive accuracy, and roughly 50% fewer parameters than non-e...
-
Towards Realistic Open-Vocabulary Remote Sensing Segmentation: Benchmark and Baseline
OVRSISBenchV2 is a realistic benchmark expanding scene and category coverage for open-vocabulary remote sensing segmentation, with Pi-Seg baseline showing strong transfer via positive-incentive noise perturbations.
-
Remote Sensing Image Super-Resolution for Imbalanced Textures: A Texture-Aware Diffusion Framework
TexADiff integrates a Relative Texture Density Map into diffusion-based super-resolution to address imbalanced textures in remote sensing images, yielding better high-frequency details and downstream task gains.
-
GeoMMBench and GeoMMAgent: Toward Expert-Level Multimodal Intelligence in Geoscience and Remote Sensing
GeoMMBench reveals deficiencies in current multimodal LLMs for geoscience tasks while GeoMMAgent demonstrates that tool-integrated agents achieve significantly higher performance.
-
Mitigating Long-Tail Bias via Prompt-Controlled Diffusion Augmentation
A prompt-controlled diffusion framework generates class-ratio-targeted synthetic layouts and domain-consistent images that, when mixed with real data, improve segmentation accuracy on long-tailed remote-sensing datase...
-
Agentic AI in Remote Sensing: Foundations, Taxonomy, and Emerging Systems
The paper delivers the first comprehensive review and unified taxonomy of agentic AI in remote sensing, covering single-agent copilots, multi-agent systems, planning mechanisms, benchmarks, and a roadmap while noting ...
-
UniGeoSeg: Towards Unified Open-World Segmentation for Geospatial Scenes
UniGeoSeg releases the first million-scale dataset for instruction-driven remote sensing segmentation and a unified model that achieves state-of-the-art results with strong zero-shot generalization.
-
SARVLM: A Vision Language Foundation Model for Semantic Understanding in SAR Imagery
SARVLM is the first vision-language foundation model for SAR, trained via domain transfer on a 1M image-text dataset and outperforming prior models on 13 benchmarks for retrieval, recognition, detection, and captioning.
-
NeSy-Route: A Neuro-Symbolic Benchmark for Constrained Route Planning in Remote Sensing
NeSy-Route supplies 10,821 optimally labeled remote-sensing route-planning tasks plus a three-level neuro-symbolic protocol that reveals major perception and planning deficits in current MLLMs.
-
Few-Shot Open-Vocabulary Remote Sensing Segmentation via Textual Inversion
Learning a pseudo-word embedding from a few support masks repairs text queries for open-vocabulary remote sensing segmentation, raising mean IoU on affected iSAID categories from 3.9 to 39.4 and beating visual-prompt ...
-
Text as Partial Constraint: Core-Residual Alignment for Robust Vision-Language Learning
Aligning images to multi-view caption cores while suppressing orthogonal residual text and disagreement-aware temperature improves robust zero-shot recognition and LVLM transfer.
-
InduceKV: Fixed-Footprint Continual Adaptation of Multimodal LLMs via Inducing KV Memories
InduceKV is a retrieval-based continual adaptation method that uses bilevel selection to build a compact set of inducing KV memories for fixed-footprint updates to multimodal LLMs.
-
FROST: Training-Free Few-Shot Segmentation with Frozen Features and Nonparametric Statistics
FROST performs training-free few-shot segmentation on remote-sensing imagery by nonparametric density-ratio classification on frozen DINOv3 features and reports 5.6 mIoU gains from one example across 17 benchmarks.
-
Seeking Consensus: Geometric-Semantic On-the-Fly Recalibration for Open-Vocabulary Remote Sensing Semantic Segmentation
SeeCo is a training-free on-the-fly recalibration method using multi-view geometric consistency and adaptive textual calibration to improve open-vocabulary semantic segmentation in remote sensing images.
-
Diffusion Model as a Generalist Segmentation Learner
DiGSeg repurposes diffusion U-Nets as generalist segmentation learners by conditioning on image-mask latents and multi-scale CLIP text features, achieving strong cross-domain performance.
-
ProtoFlow: Mitigating Forgetting in Class-Incremental Remote Sensing Segmentation via Low-Curvature Prototype Flow
ProtoFlow stabilizes class prototypes via low-curvature temporal flow to mitigate forgetting in class- and domain-incremental remote sensing segmentation.
-
NeSy-Route: A Neuro-Symbolic Benchmark for Constrained Route Planning in Remote Sensing
NeSy-Route is a 10,821-sample remote-sensing benchmark that generates constrained route-planning tasks from semantic masks plus A* search and evaluates MLLMs with a three-level symbolic protocol.
-
CrossEarth-Gate: Fisher-Guided Adaptive Tuning Engine for Efficient Adaptation of Cross-Domain Remote Sensing Semantic Segmentation
A Fisher-information-guided dynamic selection over a toolbox of LoRA, adapter, and frequency-adapter modules improves cross-domain remote sensing segmentation over static PEFT methods.
-
Towards Realistic Open-Vocabulary Remote Sensing Segmentation: Benchmark and Baseline
OVRSISBenchV2 expands open-vocabulary remote-sensing segmentation evaluation to 170K images and 128 categories, and Pi-Seg uses positive-incentive noise to improve transfer on that harder benchmark.
-
ProtoFlow: Mitigating Forgetting in Class-Incremental Remote Sensing Segmentation via Low-Curvature Prototype Flow
ProtoFlow models class prototypes as low-curvature trajectories with an explicit temporal vector field, reducing forgetting in class- and domain-incremental remote sensing segmentation by about 1.5–2.0 mIoU points.
-
RoadGIE: Towards A Global-Scale Aerial Benchmark for Generalizable Interactive Road Extraction
Introduces the largest global aerial road segmentation dataset and RoadGIE, an interactive model using topology-aware prompts that reports SOTA accuracy and connectivity on the new benchmark with a 3.7M parameter network.
-
Muon in Vision Transformers: Optimizer-Recipe Interactions and Gradient Spectra
Muon optimizer outperforms AdamW in ViT training on two image datasets, with gains that depend on data augmentation strength and are linked to wider singular-value spread in QKV gradients and prevention of late-traini...
-
ProtoFlow: Mitigating Forgetting in Class-Incremental Remote Sensing Segmentation via Low-Curvature Prototype Flow
ProtoFlow stabilizes class prototypes as low-curvature trajectories in a temporal vector field to mitigate forgetting and improve mIoU in class- and domain-incremental remote sensing segmentation.
-
State Space Models Meet Remote Sensing: A Survey
A literature survey of State Space Model methods applied to remote sensing tasks, architectures, and challenges since their introduction to the field.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.