Upload croissant_rai_IndLands.json

croissant_rai_IndLands.json

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+{
+  "@context": {
+    "@language": "en",
+    "@vocab": "https://schema.org/",
+    "arrayShape": "cr:arrayShape",
+    "citeAs": "cr:citeAs",
+    "column": "cr:column",
+    "conformsTo": "dct:conformsTo",
+    "containedIn": "cr:containedIn",
+    "cr": "http://mlcommons.org/croissant/",
+    "data": {
+      "@id": "cr:data",
+      "@type": "@json"
+    },
+    "dataBiases": "cr:dataBiases",
+    "dataCollection": "cr:dataCollection",
+    "dataType": {
+      "@id": "cr:dataType",
+      "@type": "@vocab"
+    },
+    "dct": "http://purl.org/dc/terms/",
+    "extract": "cr:extract",
+    "field": "cr:field",
+    "fileProperty": "cr:fileProperty",
+    "fileObject": "cr:fileObject",
+    "fileSet": "cr:fileSet",
+    "format": "cr:format",
+    "includes": "cr:includes",
+    "isArray": "cr:isArray",
+    "isLiveDataset": "cr:isLiveDataset",
+    "jsonPath": "cr:jsonPath",
+    "key": "cr:key",
+    "md5": "cr:md5",
+    "parentField": "cr:parentField",
+    "path": "cr:path",
+    "personalSensitiveInformation": "cr:personalSensitiveInformation",
+    "recordSet": "cr:recordSet",
+    "references": "cr:references",
+    "regex": "cr:regex",
+    "repeated": "cr:repeated",
+    "replace": "cr:replace",
+    "sc": "https://schema.org/",
+    "separator": "cr:separator",
+    "source": "cr:source",
+    "subField": "cr:subField",
+    "transform": "cr:transform",
+    "rai": "http://mlcommons.org/croissant/RAI/",
+    "prov": "http://www.w3.org/ns/prov#"
+  },
+  "@type": "sc:Dataset",
+  "distribution": [
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+      "description": "The Hugging Face git repository.",
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+      "encodingFormat": "git+https",
+      "sha256": "https://github.com/mlcommons/croissant/issues/80"
+    },
+    {
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+      "encodingFormat": "application/x-parquet",
+      "includes": "default/*/*.parquet"
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+      },
+      "@id": "default_splits",
+      "name": "default_splits",
+      "description": "Splits for the default config.",
+      "field": [
+        {
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+          "@id": "default_splits/split_name",
+          "dataType": "sc:Text"
+        }
+      ],
+      "data": [
+        {
+          "default_splits/split_name": "train"
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "@id": "default",
+      "description": "DataUploader/IndLands - 'default' subset (first 5GB)",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "default/split",
+          "dataType": "sc:Text",
+          "source": {
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+            }
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+  ],
+  "conformsTo": "http://mlcommons.org/croissant/1.1",
+  "name": "IndLands",
+  "description": "\n\t\n\t\t\n\t\tIndLands : A Spatiotemporal dataset for region-aware landslide analysis from Multi-Source Remote Sensing Imagery\n\t\n\nThis repository contains the complete workflow and supporting files for generating a landslide-prone area dataset using remote sensing and machine learning techniques. The dataset has been prepared for the following Indian states:\n\n  \n    \n      \n        Uttarakhand\n       Sikkim\n        Himachal Pradesh\n       Mizoram\n        Maharashtra\n        Karnataka… See the full description on the dataset page: https://huggingface.co/datasets/DataUploader/IndLands.",
+  "alternateName": [
+    "DataUploader/IndLands",
+    "IndLands"
+  ],
+  "creator": {
+    "@type": "Person",
+    "name": "Anonymous Data uploader",
+    "url": "https://huggingface.co/DataUploader"
+  },
+  "keywords": [
+    "feature-extraction",
+    "cc-by-4.0",
+    "< 1K",
+    "imagefolder",
+    "Image",
+    "Geospatial",
+    "Datasets",
+    "Croissant",
+    "🇺🇸 Region: US",
+    "geospatial",
+    "remote-sensing",
+    "spatial-analysis",
+    "benchmarking"
+  ],
+  "license": "https://choosealicense.com/licenses/cc-by-4.0/",
+  "url": "https://huggingface.co/datasets/DataUploader/IndLands",
+  "rai:dataLimitations": "The dataset is specially designed for environmental monitoring and disaster management purposes. Use of the satellite images should be restricted from monitoring individuals and private property. Our dataset is labeled, and in the near future, more thorough and extensive labeling will be addressed not only relying on the multispectral data. Data imbalance is an unavoidable issue which could be addressed better by more sophisticated techniques. Advanced change detection or classification techniques can be applied to synthetic Aperture Radar(SAR) images.",
+  "rai:dataBiases": "The dataset contains class imbalance, with non-landslide regions significantly outnumbering landslide samples. In addition, some geographical regions and terrain types are more represented than others due to the availability of satellite imagery, recorded events, and annotation coverage. Seasonal weather conditions, cloud cover, and sensor limitations may also introduce temporal and observational biases. These factors can lead models to favor majority classes or well-represented regions, potentially reducing performance in underrepresented terrains or rare landslide scenarios.",
+  "rai:personalSensitiveInformation": "The dataset does not contain any personal or sensitive human information such as gender, age, socio-economic status, health records, political beliefs, religion, language, or identity-related attributes. However, it includes geographical information in the form of latitude and longitude coordinates of landslide events and associated geospatial regions.",
+  "rai:dataUseCases": "This dataset has the potential of integrating data points from multiple states across India. Computer scientists and geospatial analysts can thus utilize the dataset not only for predictive modeling but also to perform targeted feature selection that accounts for local heterogeneity. For example, understanding how certain features behave differently in vegetated terrains versus non-vegetated or urbanized regions can help distinguish between natural and anthropogenic influences on slope stability. A comparative study focusing on such contrasts may reveal that vegetative cover plays a more significant role in triggering or preventing landslides in certain ecosystems, whereas built-up areas may respond more sensitively to structural disruptions or drainage issues in certain areas.",
+  "rai:dataSocialImpact": "The InLands dataset can support landslide susceptibility analysis, disaster preparedness, infrastructure planning, and early warning research in landslide-prone regions of India, potentially helping reduce loss of life and property. Its geographically diverse coverage across the Himalayas and Western Ghats also promotes broader regional representation in geospatial hazard studies.\nHowever, models developed using this dataset may inherit biases due to uneven regional distribution, annotation uncertainty, sensor limitations, and temporal imbalance. Incorrect predictions could lead to false alarms or missed hazard warnings, particularly affecting vulnerable communities in mountainous regions. The dataset contains no personally identifiable information and is derived entirely from publicly available satellite imagery and geospatial products. To mitigate misuse and improve responsible deployment, the dataset is released for research and educational purposes with transparent preprocessing, benchmarking, and documentation, while encouraging expert validation before operational use.",
+  "rai:hasSyntheticData": false
+}