Farmland Flood Monitoring Dataset under Extreme Weather
#Object detection #image recognition #pattern recognition #Agricultural monitoring #climate change research #flood prediction #disaster management
- 5000 records
- 1.5G
- JPG/PNG/JSON
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-02-04
AI Analysis & Value Prop
The current agricultural industry faces challenges from extreme climate changes, particularly the severe impacts of heavy rains and flash floods on farmland. Existing monitoring methods often rely on traditional approaches, lacking efficient real-time monitoring solutions, leading to exacerbated agricultural losses. This dataset aims to address the monitoring challenges of climate change impacts on agriculture by collecting images of farmland floods under extreme weather, providing high-quality data support for AI models. Data is collected using drones and high-resolution cameras under various weather conditions to ensure the diversity and representativeness of the images. In terms of quality control, a multi-round annotation and expert review mechanism is applied to ensure the accuracy and consistency of data annotations. Data is stored in JPEG format and organized by time and location for easy subsequent analysis and use.
Dataset Insights
Sample Examples
77dd3695**.png|1499*2000|3.85 MB
bb681fba**.png|3045*2000|6.87 MB
f67bf1e2**.png|1604*2000|2.94 MB
301830b5**.png|2092*2000|3.96 MB
fbd219c6**.png|1690*2000|3.74 MB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| flood_type | string | Describes the type of flooding in the image, such as flash flood, river flooding, etc. |
| damage_extent | string | The extent of damage to farmland due to flooding in the image, such as minor, moderate, or severe. |
| crop_type | string | The type of crops affected in the farmland as shown in the image, such as rice, wheat. |
| vegetation_health | string | Describes the health status of plants after flooding, such as healthy, damaged, or withered. |
| rain_intensity | string | The intensity of rain during the time the image was captured, with possible values like light rain, moderate rain, heavy rain. |
| human_presence | boolean | Indicating whether there is any human presence or activity 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 |
Frequently Asked Questions
- What can this dataset be used to study?
- The dataset can be used to study the impact of climate change on agriculture, especially the effects of heavy rain and flash floods on farmland.
- What kind of images does this dataset mainly include?
- The dataset mainly includes images of farmland floods under extreme weather conditions.
- Which industry is this dataset most helpful for?
- It is particularly helpful for the agriculture industry as it provides data support for flood risk management and mitigating the effects of climate change.
- How do object detection datasets like this serve agricultural research?
- Such datasets can be used to train AI models to identify the impact of floods on farmland, aiding in the development of better agricultural protection strategies.
- What problems in the agricultural sector can be addressed using this dataset?
- It can address problems such as crop loss assessment after floods, early warning systems, and agricultural disaster management.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Farmland Flood Monitoring Dataset under Extreme Weather},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/81bed39189354eaf0cc32bd9e78c65f9?cate=2},
urldate={2025-09-15},
keywords={farmland flood monitoring, extreme weather, agricultural dataset, object detection, climate change},
version={1.0}
}Using this in research? Please cite us.
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