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Harini1617/Crop-Flood-Disaster-Classification-Dataset

Crop Flood Disaster Classification Dataset The current agricultural industry faces challenges of frequent flood disasters, making it difficult to quickly assess crop damage, affecting the stability of agricultural production and supply chains. Existing solutions largely rely on manual assessments which are inefficient and highly subjective, failing to meet the need for rapid response. This dataset aims to help AI models quickly assess damage by providing images of crops with… See the full description on the dataset page: https://huggingface.co/datasets/Harini1617/Crop-Flood-Disaster-Classification-Dataset.

sourceHugging Facecc-by-nc-sa-4.0updated 1mo agoView on Hugging Face

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Dataset Card

Crop Flood Disaster Classification Dataset

The current agricultural industry faces challenges of frequent flood disasters, making it difficult to quickly assess crop damage, affecting the stability of agricultural production and supply chains. Existing solutions largely rely on manual assessments which are inefficient and highly subjective, failing to meet the need for rapid response. This dataset aims to help AI models quickly assess damage by providing images of crops with varying levels of flooding, improving the accuracy and efficiency of post-disaster assessments. Data collection is performed using a combination of high-altitude drone photography and ground sampling under different weather conditions and geographic environments. All data undergo multiple rounds of annotation and consistency checks to ensure accuracy and reliability of the labels, and are ultimately stored and organized in JPG format to facilitate subsequent machine learning and data analysis.

Technical Specifications

Field	Type	Description
file_name	string	File name
quality	string	Resolution
crop_type	string	Identify the type of crop in the image, such as rice, wheat, etc.
flood_severity	string	Determine the severity of flood impact based on the image, such as mild, moderate, and severe damage.
vegetation_health	string	Assess the health status of the crops through the image, such as normal, damaged, and dead.
lighting_condition	string	Identify the lighting conditions of the image, such as sunny, cloudy, or overcast.

Compliance Statement

<table> <tr> <td>Authorization Type</td> <td>CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)</td> </tr> <tr> <td>Commercial Use</td> <td>Requires exclusive subscription or authorization contract (monthly or per-invocation charging)</td> </tr> <tr> <td>Privacy and Anonymization</td> <td>No PII, no real company names, simulated scenarios follow industry standards</td> </tr> <tr> <td>Compliance System</td> <td>Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs</td> </tr> </table>

Source & Contact

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