{"url":"/dataset/fine-grained-grass-segmentation-dataset","name":"Fine-Grained Grass Segmentation Dataset","full_name":null,"description_markdown":"The dataset was created using high-resolution (8 m) satellite imagery from the Gaofen series (Gaofen-2 and Gaofen-6), captured in 2019 over Maduo County, China, located in the Yellow River source area. This region is known for its high-altitude, alpine grasslands, and complex terrain, with coordinates between 33°50'–35°40' N latitude and 96°50'–99°20' E longitude.\r\n\r\nWe collected two 13,872 × 13,150-pixel images from Gaofen-6 and two 7,300 × 6,905-pixel images from Gaofen-2, all containing red, green, and blue spectral bands. These images provide critical information for fine-grained grass extraction.\r\n\r\nLabeling was assisted by the X-AnyLabeling tool, supplemented with manual refinements to ensure high accuracy. Grassland coverage was classified into five levels, based on national grassland survey standards:\r\n\r\n- Low coverage (<10%)\r\n- Medium-low coverage (10%–25%)\r\n- Medium coverage (25%–50%)\r\n- Medium-high coverage (50%–75%)\r\n- High coverage (>75%)\r\n\r\nThe final dataset comprises 1,151 pairs of 256×256 patches, split into training and testing sets with an 8:2 ratio. This dataset, with its detailed and accurate labeling, is a valuable resource for advancing remote sensing applications, particularly in ecologically sensitive and high-altitude regions like the Yellow River source area.","description_withheld":null,"homepage":"https://xavierjiezou.github.io/KTDA/","introduced_date":"2024-12-09","introduced_date_note":null,"introduced_by":{"paper":"/paper/knowledge-transfer-and-domain-adaptation-for","title":"Knowledge Transfer and Domain Adaptation for Fine-Grained Remote Sensing Image Segmentation","first_author":"Shun Zhang","url":null},"license":{"name":"Apache-2.0","url":null},"modalities":[],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"}],"languages":[],"variants":["Fine-Grained Grass Segmentation Dataset"],"data_loaders":[],"num_papers_in_archive":10,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/semantic-segmentation-on-fine-grained-grass","task":"Semantic Segmentation","dataset_variant":"Fine-Grained Grass Segmentation Dataset","rows":10,"metrics":["mIoU"],"first_row_in_archive_order":{"model":"D2LS","paper":"/paper/dynamic-dictionary-learning-for-remote","metrics":{"mIoU":"51.96"},"code_links":[{"title":"XavierJiezou/D2LS","url":"https://github.com/XavierJiezou/D2LS"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/dynamic-dictionary-learning-for-remote","title":"Dynamic Dictionary Learning for Remote Sensing Image Segmentation","date":"2025-03-09","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":9,"samples_unverified":3,"pointer_only_for_licence":12,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/knowledge-transfer-and-domain-adaptation-for","title":"Knowledge Transfer and Domain Adaptation for Fine-Grained Remote Sensing Image Segmentation","date":"2024-12-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/sfa-net-semantic-feature-adjustment-network","title":"SFA-Net: Semantic Feature Adjustment Network for Remote Sensing Image Segmentation","date":"2024-09-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dinov2-learning-robust-visual-features","title":"DINOv2: Learning Robust Visual Features without Supervision","date":"2023-04-14","rows_on_this_dataset":1,"code_links":26,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":46,"samples_ran":21,"samples_unverified":25,"pointer_only_for_licence":12,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/masked-attention-mask-transformer-for","title":"Masked-attention Mask Transformer for Universal Image Segmentation","date":"2021-12-02","rows_on_this_dataset":1,"code_links":7,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":2,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/segformer-simple-and-efficient-design-for","title":"SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers","date":"2021-05-31","rows_on_this_dataset":1,"code_links":28,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":86,"samples_ran":48,"samples_unverified":38,"pointer_only_for_licence":15,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/encoder-decoder-with-atrous-separable","title":"Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation","date":"2018-02-07","rows_on_this_dataset":1,"code_links":78,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":72,"samples_ran":43,"samples_unverified":29,"pointer_only_for_licence":40,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/pyramid-scene-parsing-network","title":"Pyramid Scene Parsing Network","date":"2016-12-04","rows_on_this_dataset":1,"code_links":67,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":29,"samples_ran":7,"samples_unverified":22,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","rows_on_this_dataset":1,"code_links":487,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":757,"samples_ran":510,"samples_unverified":247,"pointer_only_for_licence":426,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/fully-convolutional-networks-for-semantic-1","title":"Fully Convolutional Networks for Semantic Segmentation","date":"2014-11-14","rows_on_this_dataset":1,"code_links":51,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":3,"samples_unverified":1,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":8,"samples_harvested":1014,"samples_ran":643,"samples_unverified":371,"pointer_only_for_licence":514,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}