IRSAMap:Towards Large-Scale, High-Resolution Land Cover Map Vectorization
With the enhancement of remote sensing image resolution and the rapid advancement of deep learning, land cover mapping is transitioning from pixel-level segmentation to object-based vector modeling. This shift demands more from deep learning models, requiring precise object boundaries and topological consistency. However, existing datasets face three main challenges: limited class annotations, small data scale, and lack of spatial structural information. To overcome these issues, we introduce IRSAMap, the first global remote sensing dataset for large-scale, high-resolution, multi-feature land cover vector mapping. IRSAMap offers four key advantages: 1) a comprehensive vector annotation system with over 1.8 million instances of 10 typical objects (e.g., buildings, roads, rivers), ensuring semantic and spatial accuracy; 2) an intelligent annotation workflow combining manual and AI-based methods to improve efficiency and consistency; 3) global coverage across 79 regions in six continents, totaling over 1,000 km; and 4) multi-task adaptability for tasks like pixel-level classification, building outline extraction, road centerline extraction, and panoramic segmentation. IRSAMap provides a standardized benchmark for the shift from pixel-based to object-based approaches, advancing geographic feature automation and collaborative modeling. It is valuable for global geographic information updates and digital twin construction. The dataset is publicly available at https://github.com/ucas-dlg/IRSAMap
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Learning without Exact Guidance: Updating Large-scale High-resolution Land Cover Maps from Low-resolution Historical Labels
Large-scale high-resolution (HR) land-cover mapping is a vital task to survey the Earth's surface and resolve many challenges facing humanity. However, it is still a non-trivial task hindered by complex ground details, v…
Pseudo LabelSemantic SegmentationWeakly supervised Semantic SegmentationWeakly-Supervised Semantic SegmentationLarge Scale High-Resolution Land Cover Mapping With Multi-Resolution Data
In this paper we propose multi-resolution data fusion methods for deep learning-based high-resolution land cover mapping from aerial imagery. The land cover mapping problem, at country-level scales, is challenging for co…
Vocal Bursts Intensity PredictionEnabling Country-Scale Land Cover Mapping with Meter-Resolution Satellite Imagery
High-resolution satellite images can provide abundant, detailed spatial information for land cover classification, which is particularly important for studying the complicated built environment. However, due to the compl…
Domain AdaptationLand Cover ClassificationPseudo LabelSegmentation Of Remote Sensing Imagery+4Cross-Resolution Land Cover Classification Using Outdated Products and Transformers
Large-scale high-resolution land cover classification is a prerequisite for constructing Earth system models and addressing ecological and resource issues. Advancements in satellite sensor technology have led to an impro…
Land Cover ClassificationLearning with less: label-efficient land cover classification at very high spatial resolution using self-supervised deep learning
Deep learning semantic segmentation methods have shown promising performance for very high 1-m resolution land cover classification, but the challenge of collecting large volumes of representative training data creates a…
Self-Supervised LearningSemantic Segmentation