Mobiusi/Terraced-Terrain-Aerial-Photography-Recognition-Dataset

Terraced Terrain Aerial Photography Recognition Dataset

The current agricultural field faces challenges in crop management and terrain monitoring, especially in complex terrains such as terraces. Traditional monitoring methods are inefficient and lack accuracy. Existing solutions largely rely on manual detection, which presents inconsistencies in annotation and low efficiency issues. This dataset aims to provide high-quality aerial image data to assist researchers and developers in improving the accuracy and efficiency of their models for object detection tasks in terraced terrain. Data collection was conducted using high-resolution aerial equipment under different climate and lighting conditions to ensure diversity and representativeness. Multiple rounds of annotation and expert reviews were conducted to ensure data quality. The data storage format is JPG, organized by image ID and category labels. The core advantages of the dataset lie in its high annotation precision and consistency, with annotation accuracy exceeding 95%. By introducing new algorithms for bounding box annotation, the performance of detection models in complex terrains has been enhanced, with accuracy improved by 15% compared to traditional methods. Moreover, the dataset's application value lies in providing accurate data for smart agricultural monitoring, optimizing crop management decisions.

Technical Specifications

Field	Type	Description
file_name	string	File name
quality	string	Resolution
terrace_type	string	The type of terrace identified, such as contour terrace, reverse slope terrace, etc.
crop_type	string	The main type of crop planted in the image, such as rice, wheat, etc.
terrace_condition	string	The physical condition and quality of the terraces, such as good, damaged, eroded, etc.
vegetation_density	float	The density of vegetation in the image, expressed as a percentage.
weather_conditions	string	The weather conditions at the time the image was taken, such as sunny, cloudy, rainy, etc.
shadow_coverage	float	The proportion of the image covered by shadows, expressed as a percentage.
human_presence	boolean	Whether there are traces of human activity in the image, such as people, buildings, etc.
water_presence	boolean	Whether there are bodies of water in the image, such as ponds, rivers, etc.

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