Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2411.01797 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866915006919999488 |
|---|---|
| author | Noppitak, Sangdaow Okafor, Emmanuel Surinta, Olarik |
| author_facet | Noppitak, Sangdaow Okafor, Emmanuel Surinta, Olarik |
| contents | Effective water resource management is crucial in agricultural regions like northeastern Thailand, where limited water retention in sandy soils poses significant challenges. In response to this issue, the Aerial Image Water Resource (AIWR) dataset was developed, comprising 800 aerial images focused on natural and artificial water bodies in this region. The dataset was created using Bing Maps and follows the standards of the Fundamental Geographic Data Set (FGDS). It includes ground truth annotations validated by experts in remote sensing, making it an invaluable resource for researchers in geoinformatics, computer vision, and artificial intelligence. The AIWR dataset presents considerable challenges, such as segmentation due to variations in the size, color, shape, and similarity of water bodies, which often resemble other land use categories. The objective of the proposed dataset is to explore advanced AI-driven methods for water body segmentation, addressing the unique challenges posed by the dataset complexity and limited size. This dataset and related research contribute to the development of novel algorithms for water management, supporting sustainable agricultural practices in regions facing similar challenges. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_01797 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AIWR: Aerial Image Water Resource Dataset for Segmentation Analysis Noppitak, Sangdaow Okafor, Emmanuel Surinta, Olarik Computer Vision and Pattern Recognition Effective water resource management is crucial in agricultural regions like northeastern Thailand, where limited water retention in sandy soils poses significant challenges. In response to this issue, the Aerial Image Water Resource (AIWR) dataset was developed, comprising 800 aerial images focused on natural and artificial water bodies in this region. The dataset was created using Bing Maps and follows the standards of the Fundamental Geographic Data Set (FGDS). It includes ground truth annotations validated by experts in remote sensing, making it an invaluable resource for researchers in geoinformatics, computer vision, and artificial intelligence. The AIWR dataset presents considerable challenges, such as segmentation due to variations in the size, color, shape, and similarity of water bodies, which often resemble other land use categories. The objective of the proposed dataset is to explore advanced AI-driven methods for water body segmentation, addressing the unique challenges posed by the dataset complexity and limited size. This dataset and related research contribute to the development of novel algorithms for water management, supporting sustainable agricultural practices in regions facing similar challenges. |
| title | AIWR: Aerial Image Water Resource Dataset for Segmentation Analysis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.01797 |