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  1. dataset_infos.json +2 -2
dataset_infos.json CHANGED
@@ -3,11 +3,11 @@
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  "description": "This dataset is designed for the task of building detection in urban environments using image segmentation. It includes high-resolution aerial or satellite images of urban areas, paired with corresponding segmentation masks that delineate building footprints. The dataset is structured into training, validation, and test sets, and is suitable for developing and evaluating models for urban climate analysis and automated building detection.",
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  "features": {
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  "image": {
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- "dtype": "Image",
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  "description": "High-resolution input images of urban environments, containing various building structures. These images serve as the input for segmentation models to detect and outline building footprints."
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  },
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  "mask": {
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- "dtype": "Image",
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  "description": "Binary segmentation masks corresponding to each input image. Each mask highlights the building footprints, where the buildings are marked in one color (e.g., white) and the background is marked in another color (e.g., black). These masks are used as ground truth for training and evaluating segmentation models."
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  }
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  },
 
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  "description": "This dataset is designed for the task of building detection in urban environments using image segmentation. It includes high-resolution aerial or satellite images of urban areas, paired with corresponding segmentation masks that delineate building footprints. The dataset is structured into training, validation, and test sets, and is suitable for developing and evaluating models for urban climate analysis and automated building detection.",
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  "features": {
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  "image": {
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+ "dtype": "image",
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  "description": "High-resolution input images of urban environments, containing various building structures. These images serve as the input for segmentation models to detect and outline building footprints."
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  },
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  "mask": {
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+ "dtype": "image",
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  "description": "Binary segmentation masks corresponding to each input image. Each mask highlights the building footprints, where the buildings are marked in one color (e.g., white) and the background is marked in another color (e.g., black). These masks are used as ground truth for training and evaluating segmentation models."
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  }
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  },