AAMS-YOLO: enhanced farmland parcel detection for high-resolution remote sensing images
Detecting farmland parcels in high-resolution remote sensing images is challenging in smallholder farming systems in China, characterized by fragmented plots, irregular shapes, and varying scales. To improve detection accuracy in these contexts, this study proposes AAMS-YOLO, a YOLO-based farmland p...
Saved in:
| Main Authors: | Binyao Wang, Ya’nan Zhou, Weiwei Zhu, Li Feng, Jinke He, Tianjun Wu, Jiancheng Luo, Xin Zhang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
|
| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2024.2432532 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Integrating Random Forest With Boundary Enhancement for Mapping Crop Planting Structure at the Parcel Level From Remote Sensing Images
by: Junyang Xie, et al.
Published: (2025-01-01) -
SBDNet: A Scale and Edge Guided Bidecoding Network for Land Parcel Extraction
by: Wei Wu, et al.
Published: (2025-01-01) -
Exploring the Effect of Item Parceling Strategies and Number of Items per Parcel on Measurement Invariance Testing in Confirmatory Factor Analysis
by: Cao, Chunhua, et al.
Published: (2025-07-01) -
Accurate Parcel Extraction Combined with Multi-Resolution Remote Sensing Images Based on SAM
by: Yong Dong, et al.
Published: (2025-04-01) -
A novel model for higher performance object detection with deep channel attention super resolution
by: Ayse Berika Varol Malkocoglu, et al.
Published: (2025-04-01)