| dc.description.abstract | Every year, the government uses polygon vectors containing crop labels to manage information about parcels and monitor their usage. However, different crops have different growing seasons, causing parcels to merge or separate throughout the year due to the planting of different crops. Therefore, these polygon vectors need to be updated whenever there are changes. However, less than 1% of parcels undergo changes, making manually checking each parcel for changes very time-consuming. As a result, using AI classifiers to detect changes from aerial images has become an important issue.
Previous research has proposed a binary image classifier to determine whether a parcel has been separated. However, this approach has two drawbacks. Firstly, the method can only identify separated parcels, not merged ones, limiting its practical application. Secondly, the model
proposed by the research did not perform well in real testing for parcel separation detection task.
To address these issues, this study proposes a solution for detecting both parcel separations and merges. The solution includes a data synthesis method to generate ”simulated parcel separation patterns”, a binary classifier to determine parcel separation or merging, and a method to apply this classifier to the detection task. The proposed data synthesis method effectively addresses the issue of extremely imbalanced datasets. We use the synthesized data to train the binary classifier, enabling it to learn the image features of parcel separation and merging, and then use the trained classifier to detect changes in real aerial images.
This study’s contributions include training a high-accuracy classifier using a small, extremely imbalanced dataset and applying it to parcel separation and merging detection tasks. Additionally, the proposed method achieved higher F1-Scores in real testing for parcel separation detection compared to existing methods and also performed well in parcel merging detection. With this classifier, we can identify less than 1% of parcels that have undergone separation or merging, saving both manpower and time. | en_US |