Geo-Information System Research Institute, Panasia, Suwon 16571, Republic of Korea.
School of Civil Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea.
Int J Environ Res Public Health. 2023 Jan 18;20(3):1770. doi: 10.3390/ijerph20031770.
Rivers are generally classified as either national or local rivers. Large-scale national rivers are maintained through systematic maintenance and management, whereas many difficulties can be encountered in the management of small-scale local rivers. Damage to embankments due to illegal farming along rivers has resulted in collapses during torrential rainfall. Various fertilizers and pesticides are applied along embankments, resulting in pollution of water and ecological spaces. Controlling such activities along riversides is challenging given the inconvenience of checking sites individually, the difficulty in checking the ease of site access, and the need to check a wide area. Furthermore, considerable time and effort is required for site investigation. Addressing such problems would require rapidly obtaining precise land data to understand the field status. This study aimed to monitor time series data by applying artificial intelligence technology that can read the cultivation status using drone-based images. With these images, the cultivated area along the river was annotated, and data were trained using the YOLOv5 and DeepLabv3+ algorithms. The performance index mAP@0.5 was used, targeting >85%. Both algorithms satisfied the target, confirming that the status of cultivated land along a river can be read using drone-based time series images.
河流一般分为国家河流和地方河流。大规模的国家河流通过系统的维护和管理来维持，而小规模的地方河流的管理可能会遇到许多困难。由于在河流沿岸非法耕种，导致堤坝受损，在暴雨期间发生了坍塌。各种化肥和农药沿着堤坝使用，导致了水和生态空间的污染。由于单独检查场地的不便、检查场地进入难易程度的困难以及需要检查大面积的场地，因此很难控制这些河边的活动。此外，现场调查需要花费大量的时间和精力。要解决这些问题，需要快速获得精确的土地数据来了解现场状况。本研究旨在通过应用人工智能技术来监测时间序列数据，该技术可以使用基于无人机的图像读取种植状态。使用这些图像对河流沿岸的耕种区域进行标注，并使用 YOLOv5 和 DeepLabv3+算法对数据进行训练。使用性能指标 mAP@0.5 作为目标，目标值为>85%。两种算法都满足了目标，证实了可以使用基于无人机的时间序列图像读取河流沿岸耕地的状态。
