{"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/agriculture-vision-a-large-aerial-image","title":"Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis","arxiv_id":"2001.01306","date":"2020-01-05","proceeding":"CVPR 2020 6","authors":["Mang Tik Chiu","Xingqian Xu","Yunchao Wei","Zilong Huang","Alexander Schwing","Robert Brunner","Hrant Khachatrian","Hovnatan Karapetyan","Ivan Dozier","Greg Rose","David Wilson","Adrian Tudor","Naira Hovakimyan","Thomas S. Huang","Honghui Shi"],"abstract":"The success of deep learning in visual recognition tasks has driven advancements in multiple fields of research. Particularly, increasing attention has been drawn towards its application in agriculture. Nevertheless, while visual pattern recognition on farmlands carries enormous economic values, little progress has been made to merge computer vision and crop sciences due to the lack of suitable agricultural image datasets. Meanwhile, problems in agriculture also pose new challenges in computer vision. For example, semantic segmentation of aerial farmland images requires inference over extremely large-size images with extreme annotation sparsity. These challenges are not present in most of the common object datasets, and we show that they are more challenging than many other aerial image datasets. To encourage research in computer vision for agriculture, we present Agriculture-Vision: a large-scale aerial farmland image dataset for semantic segmentation of agricultural patterns. We collected 94,986 high-quality aerial images from 3,432 farmlands across the US, where each image consists of RGB and Near-infrared (NIR) channels with resolution as high as 10 cm per pixel. We annotate nine types of field anomaly patterns that are most important to farmers. As a pilot study of aerial agricultural semantic segmentation, we perform comprehensive experiments using popular semantic segmentation models; we also propose an effective model designed for aerial agricultural pattern recognition. Our experiments demonstrate several challenges Agriculture-Vision poses to both the computer vision and agriculture communities. Future versions of this dataset will include even more aerial images, anomaly patterns and image channels. More information at https://www.agriculture-vision.com.","url_abs":"https://arxiv.org/abs/2001.01306v2","url_pdf":"https://arxiv.org/pdf/2001.01306v2.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":"agriculture-vision-a-large-aerial-image","repo_url":"https://github.com/dapsavoie/agricultural_satellite_classifier","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"agriculture-vision-a-large-aerial-image","repo_url":"https://github.com/josemenber/image-based-crop-anomaly-detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[{"slug":"agriculture-vision","name":"Agriculture-Vision","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2001.01306","atlas_url":"https://app.syntology.ai/?focus=2001.01306","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2001.01306"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/josemenber/image-based-crop-anomaly-detection","reach":{"status":"ok","spdx":"Apache-2.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/dapsavoie/agricultural_satellite_classifier","reach":{"status":"ok"}}],"summary":{"ran":1,"unverified":1},"by_repo_kind":{"listed":{"samples":2,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"5fb6bae1353f3a42","entry":"add_noise","repo":"josemenber/image-based-crop-anomaly-detection","repo_kind":"listed","path":"train_models.py","file_url":"https://github.com/josemenber/image-based-crop-anomaly-detection/blob/HEAD/train_models.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"5fb6bae1353f3a42"}},{"code_sha256_prefix":"135b3dc835ffe6ad","entry":"get_confirm_token","repo":"josemenber/image-based-crop-anomaly-detection","repo_kind":"listed","path":"download_images.py","file_url":"https://github.com/josemenber/image-based-crop-anomaly-detection/blob/HEAD/download_images.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"135b3dc835ffe6ad"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}