GBSS:a global building semantic segmentation dataset for large-scale remote sensing building extraction

• URL: http://arxiv.org/abs/2401.01178v1
• Date: Tue, 2 Jan 2024 12:13:35 GMT
• Title: GBSS:a global building semantic segmentation dataset for large-scale remote sensing building extraction
• Authors: Yuping Hu, Xin Huang, Jiayi Li, Zhen Zhang
• Abstract summary: We construct a Global Building Semantic dataset (The dataset will be released), which comprises 116.9k pairs of samples (about 742k buildings) from six continents. There are significant variations of building samples in terms of size and style, so the dataset can be a more challenging benchmark for evaluating the generalization and robustness of building semantic segmentation models.
• Score: 10.39943244036649
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic segmentation techniques for extracting building footprints from high-resolution remote sensing images have been widely used in many fields such as urban planning. However, large-scale building extraction demands higher diversity in training samples. In this paper, we construct a Global Building Semantic Segmentation (GBSS) dataset (The dataset will be released), which comprises 116.9k pairs of samples (about 742k buildings) from six continents. There are significant variations of building samples in terms of size and style, so the dataset can be a more challenging benchmark for evaluating the generalization and robustness of building semantic segmentation models. We validated through quantitative and qualitative comparisons between different datasets, and further confirmed the potential application in the field of transfer learning by conducting experiments on subsets.

Related papers

• SARDet-100K: Towards Open-Source Benchmark and ToolKit for Large-Scale SAR Object Detection [83.21028626585986]
  We establish a new benchmark dataset and an open-source method for large-scale SAR object detection. Our dataset, SARDet-100K, is a result of intense surveying, collecting, and standardizing 10 existing SAR detection datasets. To the best of our knowledge, SARDet-100K is the first COCO-level large-scale multi-class SAR object detection dataset ever created.
  arXiv  Detail & Related papers  (2024-03-11T09:20:40Z)
• Rethinking Transformers Pre-training for Multi-Spectral Satellite Imagery [78.43828998065071]
  Recent advances in unsupervised learning have demonstrated the ability of large vision models to achieve promising results on downstream tasks. Such pre-training techniques have also been explored recently in the remote sensing domain due to the availability of large amount of unlabelled data. In this paper, we re-visit transformers pre-training and leverage multi-scale information that is effectively utilized with multiple modalities.
  arXiv  Detail & Related papers  (2024-03-08T16:18:04Z)
• Multi-task deep learning for large-scale building detail extraction from high-resolution satellite imagery [13.544826927121992]
  Multi-task Building Refiner (MT-BR) is an adaptable neural network tailored for simultaneous extraction of building details from satellite imagery. For large-scale applications, we devise a novel spatial sampling scheme that strategically selects limited but representative image samples. MT-BR consistently outperforms other state-of-the-art methods in extracting building details across various metrics.
  arXiv  Detail & Related papers  (2023-10-29T04:43:30Z)
• Building Extraction from Remote Sensing Images via an Uncertainty-Aware Network [18.365220543556113]
  Building extraction plays an essential role in many applications, such as city planning and urban dynamic monitoring. We propose a novel and straightforward Uncertainty-Aware Network (UANet) to alleviate this problem. Results demonstrate that the proposed UANet outperforms other state-of-the-art algorithms by a large margin.
  arXiv  Detail & Related papers  (2023-07-23T12:42:15Z)
• A diverse large-scale building dataset and a novel plug-and-play domain generalization method for building extraction [2.578242050187029]
  We introduce a new building dataset and propose a novel domain generalization method to facilitate the development of building extraction from remote sensing images. The WHU-Mix building dataset consists of a training/validation set containing 43,727 diverse images collected from all over the world, and a test set containing 8402 images from five other cities on five continents. To further improve the generalization ability of a building extraction model, we propose a domain generalization method named batch style mixing (BSM)
  arXiv  Detail & Related papers  (2022-08-22T01:43:13Z)
• TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual Environments [84.6017003787244]
  This work proposes a synthetic data generation pipeline to address the difficulties and domain-gaps present in simulated datasets. We show that using annotations and visual cues from existing datasets, we can facilitate automated multi-modal data generation.
  arXiv  Detail & Related papers  (2022-08-16T20:46:08Z)
• MetaGraspNet: A Large-Scale Benchmark Dataset for Scene-Aware Ambidextrous Bin Picking via Physics-based Metaverse Synthesis [72.85526892440251]
  We introduce MetaGraspNet, a large-scale photo-realistic bin picking dataset constructed via physics-based metaverse synthesis. The proposed dataset contains 217k RGBD images across 82 different article types, with full annotations for object detection, amodal perception, keypoint detection, manipulation order and ambidextrous grasp labels for a parallel-jaw and vacuum gripper. We also provide a real dataset consisting of over 2.3k fully annotated high-quality RGBD images, divided into 5 levels of difficulties and an unseen object set to evaluate different object and layout properties.
  arXiv  Detail & Related papers  (2022-08-08T08:15:34Z)
• Detection Hub: Unifying Object Detection Datasets via Query Adaptation on Language Embedding [137.3719377780593]
  A new design (named Detection Hub) is dataset-aware and category-aligned. It mitigates the dataset inconsistency and provides coherent guidance for the detector to learn across multiple datasets. The categories across datasets are semantically aligned into a unified space by replacing one-hot category representations with word embedding.
  arXiv  Detail & Related papers  (2022-06-07T17:59:44Z)
• Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation [94.16816278191477]
  We present a framework for semi-adaptive and domain-supervised semantic segmentation. It is enhanced by self-supervised monocular depth estimation trained only on unlabeled image sequences. We validate the proposed model on the Cityscapes dataset.
  arXiv  Detail & Related papers  (2021-08-28T01:33:38Z)
• Continental-Scale Building Detection from High Resolution Satellite Imagery [5.56205296867374]
  We study variations in architecture, loss functions, regularization, pre-training, self-training and post-processing that increase instance segmentation performance. Experiments were carried out using a dataset of 100k satellite images across Africa containing 1.75M manually labelled building instances. We report novel methods for improving performance of building detection with this type of model, including the use of mixup.
  arXiv  Detail & Related papers  (2021-07-26T15:48:14Z)
• Holistic Multi-View Building Analysis in the Wild with Projection Pooling [18.93067906200084]
  We address six different classification tasks related to fine-grained building attributes. Tackling such a remote building analysis problem became possible only recently due to growing large-scale datasets of urban scenes. We introduce a new benchmarking dataset, consisting of 49426 images (top-view and street-view) of 9674 buildings.
  arXiv  Detail & Related papers  (2020-08-23T13:49:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.