{"id":13651694,"url":"https://github.com/lironui/ABCNet","last_synced_at":"2025-04-22T22:31:57.077Z","repository":{"id":50516188,"uuid":"334885174","full_name":"lironui/ABCNet","owner":"lironui","description":"The semantic segmentation of remote sensing images","archived":false,"fork":false,"pushed_at":"2023-09-29T11:02:29.000Z","size":5545,"stargazers_count":22,"open_issues_count":5,"forks_count":2,"subscribers_count":3,"default_branch":"main","last_synced_at":"2024-01-29T09:16:02.954Z","etag":null,"topics":["efficient-model","isprs","potsdam","real-time","remote-sensing","segmentation","semantic-segmentation","uav","uavid","vaihingen"],"latest_commit_sha":null,"homepage":"https://lironui.github.io/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/lironui.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2021-02-01T08:46:30.000Z","updated_at":"2024-01-19T13:19:12.000Z","dependencies_parsed_at":"2023-10-01T11:15:43.408Z","dependency_job_id":null,"html_url":"https://github.com/lironui/ABCNet","commit_stats":{"total_commits":28,"total_committers":2,"mean_commits":14.0,"dds":0.1071428571428571,"last_synced_commit":"3067b46f74586791ec54add07a31a7ac65dae3a5"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lironui%2FABCNet","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lironui%2FABCNet/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lironui%2FABCNet/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/lironui%2FABCNet/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/lironui","download_url":"https://codeload.github.com/lironui/ABCNet/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":223906327,"owners_count":17223045,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["efficient-model","isprs","potsdam","real-time","remote-sensing","segmentation","semantic-segmentation","uav","uavid","vaihingen"],"created_at":"2024-08-02T02:00:51.604Z","updated_at":"2024-11-10T02:31:13.130Z","avatar_url":"https://github.com/lironui.png","language":"Python","readme":"#  ABCNet: Attentive Bilateral Contextual Network for Efficient Semantic Segmentation of Fine-Resolution Remote Sensing Images\n\n⭐ [Welcome to my HomePage](https://lironui.github.io/) ⭐ \n\nIn this repository, we implement the Attentive Bilateral Contextual Network which contains a spatial path and a contextual path to fully capture the long-range relationships and fine-grained details in fine-resolution remote sensing images. \n\nThe detailed results can be seen in the [ABCNet: Attentive Bilateral Contextual Network for Efficient Semantic Segmentation of Fine-Resolution Remote Sensing Images](https://www.sciencedirect.com/science/article/pii/S0924271621002379).\n\nThe training and testing code can refer to [GeoSeg](https://github.com/WangLibo1995/GeoSeg).\n\nThe related repositories include:\n* [MACU-Net](https://github.com/lironui/MACU-Net)-\u003eA modified version of U-Net.\n* [BANet](https://github.com/lironui/BANet)-\u003eA Transformer-based segmentation network.\n* [MAResU-Net](https://github.com/lironui/MAResU-Net)-\u003eA ResNet-based network with attention mechanism.\n* [Multi-Attention-Network](https://github.com/lironui/Multi-Attention-Network)-\u003eA network with multi kernel attention mechanism.\n\nIf our code is helpful to you, please cite:\n\n`R. Li, S. Zheng, C. Zhang, C. Duan, L. Wang, and P. M. Atkinson, \"ABCNet: Attentive bilateral contextual network for efficient semantic segmentation of Fine-Resolution remotely sensed imagery,\" ISPRS Journal of Photogrammetry and Remote Sensing, vol. 181, pp. 84-98, 2021/11/01/ 2021, doi: https://doi.org/10.1016/j.isprsjprs.2021.09.005.`\n\nNetwork:\n------- \n![network](https://github.com/lironui/ABCNet/blob/main/figure/network.png)  \nFig. 1.  The overall architecture of ABCNet.\n\nResult:\n------- \nThe result on the [UAVid dataset](https://uavid.nl/) can seen from [here, where the user name is **lironui**](https://competitions.codalab.org/competitions/25224#results) or [here](https://competitions.codalab.org/competitions/public_submissions/25224) and can be downloaded by [**this link**](https://competitions.codalab.org/my/competition/submission/904615/input.zip):\n\n| Method    | building | tree     | clutter   | road     | vegetation | static car | moving car | human    | mIoU     | \n|-----------|----------|----------|-----------|----------|------------|------------|------------|----------|----------| \n| MSD       | 79.8     | 74.5     | 57.0      | 74.0     | 55.9       | 32.1       | 62.9       | **19.7** | 57.0     | \n| Fast-SCNN | 75.7     | 71.5     | 44.2      | 61.6     | 43.4       | 19.5       | 51.6       | 0.0      | 45.9     | \n| BiSeNet   | 85.7     | 78.3     | 64.7      | 61.1     | **77.3**   | **63.4**   | 48.6       | 17.5     | 61.5     | \n| SwiftNet  | 85.3     | 78.2     | 64.1      | 61.5     | 76.4       | 62.1       | 51.1       | 15.7     | 61.1     | \n| ShelfNet  | 76.9     | 73.2     | 44.1      | 61.4     | 43.4       | 21.0       | 52.6       | 3.6      | 47.0     |  \n| ABCNet    | **86.4** | **79.9** | **67.4**  | **81.2** | 63.1       | 48.4       | **69.8**   | 13.9     | **63.8** | \n\n\n![Result](https://github.com/lironui/ABCNet/blob/main/figure/UAVid.png)  \nFig. 2.  The experimental results on the UAVid test set. The first column illustrates the input RGB images, the second column depicts the outputs of MSD and the third column shows the predictions of our ABCNet. \n\n","funding_links":[],"categories":["Computer Vision 🤖"],"sub_categories":["Modules 🧩"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flironui%2FABCNet","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flironui%2FABCNet","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flironui%2FABCNet/lists"}