{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/lsknet-a-foundation-lightweight-backbone-for","title":"LSKNet: A Foundation Lightweight Backbone for Remote Sensing","arxiv_id":"2403.11735","date":"2024-03-18","proceeding":null,"authors":["YuXuan Li","Xiang Li","Yimian Dai","Qibin Hou","Li Liu","Yongxiang Liu","Ming-Ming Cheng","Jian Yang"],"abstract":"Remote sensing images pose distinct challenges for downstream tasks due to their inherent complexity. While a considerable amount of research has been dedicated to remote sensing classification, object detection and semantic segmentation, most of these studies have overlooked the valuable prior knowledge embedded within remote sensing scenarios. Such prior knowledge can be useful because remote sensing objects may be mistakenly recognized without referencing a sufficiently long-range context, which can vary for different objects. This paper considers these priors and proposes a lightweight Large Selective Kernel Network (LSKNet) backbone. LSKNet can dynamically adjust its large spatial receptive field to better model the ranging context of various objects in remote sensing scenarios. To our knowledge, large and selective kernel mechanisms have not been previously explored in remote sensing images. Without bells and whistles, our lightweight LSKNet sets new state-of-the-art scores on standard remote sensing classification, object detection and semantic segmentation benchmarks. Our comprehensive analysis further validated the significance of the identified priors and the effectiveness of LSKNet. The code is available at https://github.com/zcablii/LSKNet.","url_abs":"https://arxiv.org/abs/2403.11735v5","url_pdf":"https://arxiv.org/pdf/2403.11735v5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"lsknet-a-foundation-lightweight-backbone-for","repo_url":"https://github.com/zcablii/lsknet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"lsknet-a-foundation-lightweight-backbone-for","repo_url":"https://github.com/zcablii/Large-Selective-Kernel-Network","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"change-detection","task_name":"Change Detection"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-in-aerial-images","task_name":"Object Detection In Aerial Images"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"dilated-convolution","method_name":"Dilated Convolution"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"selective-kernel","method_name":"Selective Kernel"},{"method_slug":"selective-kernel-convolution","method_name":"Selective Kernel Convolution"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/change-detection-on-levir-cd","task":"Change Detection","dataset":"LEVIR-CD","model":"LSKNet","rank_in_archive_order":7,"of":28,"metrics":{"F1":"92.27","F1-score":"92.27","IoU":"85.65","Precision":"93.34","Recall":"91.23"},"uses_additional_data":false},{"leaderboard":"/sota/change-detection-on-s2looking","task":"Change Detection","dataset":"S2Looking","model":"LSKNet-S","rank_in_archive_order":4,"of":11,"metrics":{"F1-Score":"67.52","IoU":"50.96","Precision":"71.90","Recall":"63.64"},"uses_additional_data":false},{"leaderboard":"/sota/object-detection-in-aerial-images-on-dota-1","task":"Object Detection In Aerial Images","dataset":"DOTA","model":"LSKNet-S*","rank_in_archive_order":7,"of":58,"metrics":{"mAP":"81.85%"},"uses_additional_data":true},{"leaderboard":"/sota/object-detection-in-aerial-images-on-dota-1","task":"Object Detection In Aerial Images","dataset":"DOTA","model":"LSKNet-S","rank_in_archive_order":10,"of":58,"metrics":{"mAP":"81.64%"},"uses_additional_data":true},{"leaderboard":"/sota/object-detection-in-aerial-images-on-dota-1","task":"Object Detection In Aerial Images","dataset":"DOTA","model":"LSKNet-T","rank_in_archive_order":11,"of":58,"metrics":{"mAP":"81.37%"},"uses_additional_data":true},{"leaderboard":"/sota/semantic-segmentation-on-isprs-potsdam","task":"Semantic Segmentation","dataset":"ISPRS Potsdam","model":"LSKNet-S","rank_in_archive_order":5,"of":20,"metrics":{"Mean F1":"93.1","Mean IoU":"87.2","Overall Accuracy":"92.0"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-isprs-vaihingen","task":"Semantic Segmentation","dataset":"ISPRS Vaihingen","model":"LSKNet-S","rank_in_archive_order":1,"of":12,"metrics":{"Average F1":"91.8","Category mIoU":"85.1","Overall Accuracy":"93.6"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-isprs-vaihingen","task":"Semantic Segmentation","dataset":"ISPRS Vaihingen","model":"LSKNet-T","rank_in_archive_order":2,"of":12,"metrics":{"Average F1":"91.7","Category mIoU":"84.9","Overall Accuracy":"93.6"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-uavid","task":"Semantic Segmentation","dataset":"UAVid","model":"LSKNet-S","rank_in_archive_order":5,"of":10,"metrics":{"Mean IoU":"70.0"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-uavid","task":"Semantic Segmentation","dataset":"UAVid","model":"LSKNet-T","rank_in_archive_order":6,"of":10,"metrics":{"Mean IoU":"69.3"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2403.11735","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}