diff --git a/static/data/dataset_history.json b/static/data/dataset_history.json index 8bba37f..6e799d7 100644 --- a/static/data/dataset_history.json +++ b/static/data/dataset_history.json @@ -1,6 +1,6 @@ { "_readme": "Hand-maintained going forward — do not regenerate. To add a snapshot, run `python3 scripts/append_dataset_snapshot.py` (counts the live static/data/{datasets,hf_datasets}.json the same way src/lib/datasets.ts computeDatasetStats does: top-level datasets only, iNatAg-mini's images excluded) or append to `points` by hand: { period: \"\", date: \"\", datasetCount: , imageCount: }.", - "generatedAt": "2026-09-14", + "generatedAt": "2026-09-15", "annotations": [ { "atPeriod": "May 30", @@ -153,6 +153,12 @@ "date": "2026-09-14", "datasetCount": 299, "imageCount": 6533431 + }, + { + "period": "Sep 16", + "date": "2026-09-15", + "datasetCount": 306, + "imageCount": 6766197 } ] } diff --git a/static/data/hf_datasets.json b/static/data/hf_datasets.json index 2074e30..e673065 100644 --- a/static/data/hf_datasets.json +++ b/static/data/hf_datasets.json @@ -12393,7 +12393,7 @@ "annotation_format": "segmentationMask", "num_images": 126, "documentation": "https://doi.org/10.1371/journal.pone.0256340", - "examples_image_url": "/img/agml/sample_images/pheno4d_point_cloud_segmentation_sample.png", + "examples_image_url": "/img/agml/sample_images/pheno4d_point_cloud_segmentation_sample.webp", "point_cloud_sample_url": "/data/point_cloud_samples/pheno4d_point_cloud_segmentation_sample.json", "license": "cc-by-4.0", "citation": "Schunck, D., Magistri, F., Rosu, R.A., et al. (2021). Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis. PLOS ONE, 16(8).", @@ -12401,5 +12401,225 @@ "zip_size_bytes": 5293657341, "stats_mean": null, "stats_std": null + }, + { + "name": "Sen2_LULC", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "land_use_segmentation", + "location": [ + "Central Indian region, India" + ], + "lat_lon": [], + "country": "India", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "Sentinel-2 (10 m resolution, Level-2A), satellite" + ], + "collection_period": "February-March 2021", + "platform": "satellite", + "input_data_format": "parquet", + "annotation_format": "segmentationMask", + "num_images": 213758, + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2023.109724", + "citation": "Sawant, Suraj; Garg, Rahul Dev; Meshram, Vishal; Mistry, Shrayank (2023), “Sen-2 LULC ”, Mendeley Data, V3, doi: 10.17632/f4ky6ks248.3", + "zip_size_bytes": 3099453834, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/Sen2_LULC", + "examples_image_url": "/img/agml/sample_images/Sen2_LULC_sample.webp" + }, + { + "name": "pine_wilt_segmentation", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "disease_detection", + "location": [ + "Jinju, South Korea" + ], + "lat_lon": [], + "country": "South Korea", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "pine" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Korea Aerospace Research Institute (KARI) aerial imagery, 5-cm resolution" + ], + "collection_period": "January 2023", + "platform": "aerial", + "input_data_format": "parquet", + "annotation_format": "segmentationMask", + "num_images": 2238, + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.ecoinf.2025.103421", + "citation": "Ha, Uirin; Kim, Hyungho; Kim, Seunguk; Choe, Hyeyeong (2025), “Enhanced pine wilt disease outbreak prediction: integrating deep learning-detected infected trees with species distribution modeling”, Mendeley Data, V3, doi: 10.17632/swpp8jxymv.3", + "zip_size_bytes": 1907862607, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/pine_wilt_segmentation", + "examples_image_url": "/img/agml/sample_images/pine_wilt_segmentation_sample.webp" + }, + { + "name": "tree_crown_segmentation", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "crop_segmentation", + "location": [ + "Huayi Agricultural Camellia oleifera Plantation, Chenjiafang Town, Xinshao County, Shaoyang City, Hunan Province, China" + ], + "lat_lon": [], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "Camellia oleifera" + ], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "DJI Mavic 3 M (RGB: 20 MP, Multispectral: 5 MP per band)" + ], + "collection_period": "November 4, 2023", + "platform": "uav", + "input_data_format": "parquet", + "annotation_format": "segmentationMask", + "num_images": 595, + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.ecoinf.2026.103868", + "citation": "peng, . yongkang . (2026). experimental data [Figure]. Zenodo. https://doi.org/10.5281/zenodo.18505981", + "zip_size_bytes": 352006130, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/tree_crown_segmentation", + "examples_image_url": "/img/agml/sample_images/tree_crown_segmentation_sample.webp" + }, + { + "name": "subalpine_forest_segmentation", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "", + "location": [ + "Wanglang National Nature Reserve, Pingwu County, Sichuan Province, China" + ], + "lat_lon": [], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [ + "DJI Mavic 2, 1-in. CMOS camera, UAV" + ], + "collection_period": "June 15, 2020", + "platform": "uav", + "input_data_format": "parquet", + "annotation_format": "segmentationMask", + "num_images": 11336, + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.ecoinf.2025.103111", + "citation": "Shi, weibo (2025), “CNN model to map vegetation classification in a subalpine coniferous forest using UAV imagery”, Mendeley Data, V2, doi: 10.17632/d9f4m2735b.2", + "zip_size_bytes": 3046798213, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/subalpine_forest_segmentation", + "examples_image_url": "/img/agml/sample_images/subalpine_forest_segmentation_sample.webp" + }, + { + "name": "Alphonso_Mango_phenotyping", + "machine_learning_task": "ground_truth", + "agricultural_task": "", + "location": [ + "Yelachahalli, Yelawala, Mysuru" + ], + "lat_lon": [ + "12.379, 76.524" + ], + "country": "India", + "environment": "lab", + "real_or_synthetic": "real", + "crop_types": [ + "mango" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Logitech C270 HD Webcam, 720p/30 fps, fixed focus, 60-degree diagonal field of vision" + ], + "collection_period": "May/June 2022", + "platform": "fixed", + "input_data_format": "parquet", + "annotation_format": "numericMeasurements", + "num_images": 100, + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2023.109778", + "citation": "Prabhu, Akshatha; Rani, N.Shobha (2023), “Alphonso Mangoes Image Dataset”, Mendeley Data, V1, doi: 10.17632/8sjny373pz.1", + "zip_size_bytes": 32519190, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/Alphonso_Mango_phenotyping", + "examples_image_url": "/img/agml/sample_images/Alphonso_Mango_phenotyping_sample.webp" + }, + { + "name": "rural_LUCC_segmentation", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "land_use_segmentation", + "location": [ + "Jiangning District, Nanjing City, Jiangsu Province" + ], + "lat_lon": [], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [], + "collection_period": "2024", + "platform": "satellite", + "input_data_format": "parquet", + "annotation_format": "segmentationMask", + "num_images": 2042, + "classes": [], + "license": "Apache-2.0", + "documentation": "https://doi.org/10.1016/j.ecoinf.2025.103078", + "citation": "https://www.kaggle.com/datasets/vvghigh/ruraluse", + "zip_size_bytes": 18017979, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/rural_LUCC_segmentation", + "examples_image_url": "/img/agml/sample_images/rural_LUCC_segmentation_sample.webp" + }, + { + "name": "horseradish_weed_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "weed_detection", + "location": ["Illinois, United States of America"], + "lat_lon": [], + "country": "United States of America", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "horseradish" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "iPhone 13 Mini", + "Farm-ng Amiga" + ], + "collection_period": "June 2024", + "platform": "", + "input_data_format": "parquet", + "annotation_format": "boundingBox", + "num_images": 2697, + "classes": [ + "horseradish", + "weed" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3389/fagro.2026.1777087", + "citation": "Pagadala, Abhinav; Shajahan, Sunoj (2026), “Horseradish and weed dataset from commercial fields in Southern Illinois”, Mendeley Data, V1, doi: 10.17632/fcf7brsfm6.1", + "zip_size_bytes": 506390258, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/horseradish_weed_detection", + "examples_image_url": "/img/agml/sample_images/horseradish_weed_detection_sample.webp" } -] \ No newline at end of file +] diff --git a/static/img/agml/sample_images/Alphonso_Mango_phenotyping_sample.webp b/static/img/agml/sample_images/Alphonso_Mango_phenotyping_sample.webp new file mode 100644 index 0000000..b9862cc Binary files /dev/null and b/static/img/agml/sample_images/Alphonso_Mango_phenotyping_sample.webp differ diff --git a/static/img/agml/sample_images/Sen2_LULC_sample.webp b/static/img/agml/sample_images/Sen2_LULC_sample.webp new file mode 100644 index 0000000..6b41a6b Binary files /dev/null and b/static/img/agml/sample_images/Sen2_LULC_sample.webp differ diff --git a/static/img/agml/sample_images/horseradish_weed_detection_sample.webp b/static/img/agml/sample_images/horseradish_weed_detection_sample.webp new file mode 100644 index 0000000..369f73d Binary files /dev/null and b/static/img/agml/sample_images/horseradish_weed_detection_sample.webp differ diff --git a/static/img/agml/sample_images/pheno4d_point_cloud_segmentation_sample.png b/static/img/agml/sample_images/pheno4d_point_cloud_segmentation_sample.png deleted file mode 100644 index 3f0ea6a..0000000 Binary files a/static/img/agml/sample_images/pheno4d_point_cloud_segmentation_sample.png and /dev/null differ diff --git a/static/img/agml/sample_images/pheno4d_point_cloud_segmentation_sample.webp b/static/img/agml/sample_images/pheno4d_point_cloud_segmentation_sample.webp new file mode 100644 index 0000000..2e3968c Binary files /dev/null and b/static/img/agml/sample_images/pheno4d_point_cloud_segmentation_sample.webp differ diff --git a/static/img/agml/sample_images/pine_wilt_segmentation_sample.webp b/static/img/agml/sample_images/pine_wilt_segmentation_sample.webp new file mode 100644 index 0000000..e51696e Binary files /dev/null and b/static/img/agml/sample_images/pine_wilt_segmentation_sample.webp differ diff --git a/static/img/agml/sample_images/rural_LUCC_segmentation_sample.webp b/static/img/agml/sample_images/rural_LUCC_segmentation_sample.webp new file mode 100644 index 0000000..ec2bece Binary files /dev/null and b/static/img/agml/sample_images/rural_LUCC_segmentation_sample.webp differ diff --git a/static/img/agml/sample_images/subalpine_forest_segmentation_sample.webp b/static/img/agml/sample_images/subalpine_forest_segmentation_sample.webp new file mode 100644 index 0000000..e8082fd Binary files /dev/null and b/static/img/agml/sample_images/subalpine_forest_segmentation_sample.webp differ diff --git a/static/img/agml/sample_images/tree_crown_segmentation_sample.webp b/static/img/agml/sample_images/tree_crown_segmentation_sample.webp new file mode 100644 index 0000000..637e49c Binary files /dev/null and b/static/img/agml/sample_images/tree_crown_segmentation_sample.webp differ