LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation

By Junjue Wang, Zhuo Zheng, Ailong Ma, Xiaoyan Lu, Yanfei Zhong. NeurIPS Datasets and Benchmarks, 2021

670 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/1ec56ae0d0e2afddee24737b72ac507fbd955186

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

GeoChat:Grounded Large Vision-Language Model for Remote Sensing (2023, methodology): “To address this, we propose to utilize ViTAE-RVSA [31] model, pre-trained on the LoveDA dataset [32], which encompasses the required important classes.”
RingMo: A Remote Sensing Foundation Model With Masked Image Modeling (2023, background): “1) Statistical Information: Some of the images in the dataset are selected from public datasets in the RS ﬁeld, such as [4], [5], [37], [40], [64], [65], and the other images are obtained from China’s Gaofen-2 satellite.”
UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery (2021, positive): “(Li et al., 2021c) but also outperform recent Transformer-based lightweight networks, such as Segmenter (Strudel et al., 2021) and BANet (Wang et al., 2021b).”
OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping (2022, methodology): “Apart from supervised learning, these datasets have been used in more realistic problems, including transfer learning [49], semi-supervised learning [4] and weakly supervised learning [36, 21].”
Advancing Plain Vision Transformer Toward Remote Sensing Foundation Model (2022, methodology): “Besides the Potsdam2 and iSAID [70] datasets following [10], we also adopt a recently proposed domain adaptation segmentation dataset — LoveDA [71].”
EarthGPT: A Universal Multimodal Large Language Model for Multisensor Image Comprehension in Remote Sensing Domain (2024, background): “The visual-enhanced perception mechanism consists of two modules as shown below.”
RemoteCLIP: A Vision Language Foundation Model for Remote Sensing (2023, methodology): “3) Segmentation Data ( SEG-4 ): Four popular remote sensing semantic segmentation datasets, including Vaihin-gen [81], Postdam [82], iSAID [83], and LoveDA [84], are adopted and translated via M2B then B2C.”