IRSAMap:Towards Large-Scale, High-Resolution Land Cover Map Vectorization

TL;DR

IRSAMap introduces a comprehensive, large-scale, high-resolution remote sensing dataset with detailed vector annotations across multiple regions, enabling advanced object-based land cover mapping and supporting diverse geographic information tasks.

Contribution

This paper presents IRSAMap, the first global dataset for high-resolution land cover vector mapping, addressing data limitations and supporting multi-task geographic feature extraction.

Findings

Over 1.8 million object instances annotated

Global coverage across 79 regions on six continents

Supports multiple land cover mapping tasks

Abstract

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