Challenge Introduction:
The objective of this challenge is to develop an artificial intelligence system capable of detecting river boundary encroachments using remote sensing data, such as satellite and drone images. This objective encompasses the following aspects:
Protection of Water Resources:
– Rivers, being vital water resources, require protection and management. This system can identify unauthorized encroachments and changes within river boundaries, helping to reduce pollution and illegal exploitation.
Flood Risk Prevention:
– Encroachments on river boundaries can alter water flow and increase the risk of flooding. Early detection of these encroachments can help prevent floods and the resulting damage.
Habitat Conservation:
– Rivers and their surrounding areas are habitats for many plant and animal species. Protecting river boundaries helps preserve these habitats and biodiversity.
Efficient Management:
– Providing precise and reliable tools for identifying river boundary encroachments assists governmental and environmental organizations in making better management decisions and taking appropriate legal actions.
Ultimately, this challenge can serve as an effective tool in the sustainable management of natural resources and environmental protection, aiding the global community in achieving sustainable development goals.
Expected Output:
Identification of river boundary encroachments.
Evaluation Method:
The proposed algorithms in this challenge should be comprehensively and accurately evaluated to ensure that the models can detect river boundary encroachments with appropriate precision and efficiency. Evaluation criteria include:
- Detection Accuracy:
– Definition: The percentage of samples correctly identified by the model.
– Evaluation: Calculate the ratio of correctly detected samples to the total samples. Models with higher accuracy will receive higher scores.
- Processing Time:
– Definition: The time required by the model to process and analyze each image.
– Evaluation: Models capable of faster processing and requiring less time to analyze images are preferred, especially in time-sensitive applications.
- Efficiency:
– Definition: The amount of computational resources needed to run the model.
– Evaluation: Assess the memory, CPU, and GPU usage during model execution. Models that provide better performance with fewer resources will score higher.
- Interpretability:
– Definition: The model’s ability to explain and interpret output results in a way that is understandable to non-specialist users.
– Evaluation: Models that offer clear and interpretable outputs and can explain the reasons behind their detections are preferred.
- Robustness and Performance under Different Conditions:
– Definition: The model’s ability to maintain accuracy and efficiency under various conditions and with new and unseen data.
– Evaluation: Test the model with new and diverse data, examining its performance under different conditions (e.g., seasonal changes, lighting variations).
- Scalability and Deployability:
– Definition: The model’s ability to scale and be deployed in real-world, large-scale environments.
– Evaluation: Models that can be easily scaled and deployed in complex systems will receive higher scores.
Overall Evaluation Approach:
– Testing with Validated Data Sets: Use test data sets that include labeled images from various regions and under different conditions.
– Experimental Evaluation: Run models in real-world conditions and examine practical results.
– Feedback from Experts: Obtain feedback from environmental and water resource experts to assess the applicability and accuracy of the models.
This challenge aims to advance the use of artificial intelligence in environmental monitoring, providing practical tools for managing and protecting water resources effectively.
