Datasets:
file_name stringclasses 4 values | quality stringclasses 4 values | damage_type stringclasses 3 values | crop_type stringclasses 3 values | damage_severity stringclasses 4 values | flood_water_level stringclasses 4 values | visibility_condition stringclasses 4 values | infrastructure_damage stringclasses 3 values | vegetation_health stringclasses 4 values |
|---|---|---|---|---|---|---|---|---|
745c45d87d76af9a85e7b662c9ede230.png | 1688*2000 | Water damage | Corn | Mild | About 20 cm | Good | No significant infrastructure damage | Vegetation health is good |
760d152ec5e1c7018b59f00215bad72f.png | 1604*2000 | Farmland flooded | Rice | Severe | Reached plant height | Good visibility, no significant haze | Utility poles intact | Vegetation at risk of flooding |
804d3416f8b65411e15883a66643f0fa.png | 2092*2000 | Farmland flooded | Corn | Moderate | Near crop roots | Good, no significant haze | No significant infrastructure damage | Gramineous crops bent, some plants damaged |
c06f4314701b82fcbddc44588fd3b38e.png | 1499*2000 | flood submersion | corn | moderate severity | near crop height | visibility normal, sunny | no significant infrastructure damage | some vegetation submerged, health deteriorating |
Agricultural Flood Disaster Insurance Claim Image Dataset
The current agricultural industry faces the frequent problem of flood disasters, causing severe economic losses to farmland. Traditional claim methods rely on manual inspection, which is inefficient and prone to errors. Existing claim systems are mostly manual audits, lacking efficient automated recognition tools. This dataset aims to help AI systems achieve automatic recognition and damage assessment by providing high-quality flood disaster images, thus improving claim efficiency. The dataset is collected by professionals in real flood disaster environments, captured using high-resolution cameras to ensure image quality. We have adopted measures such as multiple rounds of labeling and consistency checks for quality control, ensuring the precision and consistency of data labeling. Data is stored in JPEG format, organized by time and location, facilitating subsequent analysis and use.
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| damage_type | string | Identify the type of damage caused to farmland by flood disaster in the image. |
| crop_type | string | Identify the type of crop affected in the image. |
| damage_severity | string | Assess the severity of damage caused by flood disaster in the image. |
| flood_water_level | float | Estimate the height of flood water in the image. |
| visibility_condition | string | Describe the visibility conditions in the image, such as haze or sunlight. |
| infrastructure_damage | string | Identify and describe any infrastructure damage in the image. |
| vegetation_health | string | Assess the health condition of vegetation in the image. |
Compliance Statement
| Authorization Type | CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Source & Contact
If you need more dataset details, please visit Mobiusi. or contact us via contact@mobiusi.com
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