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Abstract ：
Identifying agricultural machinery operations is crucial for enhancing agricultural productivity and promoting the transition to data-driven agriculture. Current research focuses solely on administrative divisions, overlooking the links between machinery movement, natural spatial patterns, and spatiotemporal dependencies. The direct clustering of GNSS points is inefficient and incurs substantial computational costs. In response to these challenges, we introduce an unsupervised clustering method based on multiscale spatiotemporal partitioning, which systematically integrates spatial and temporal dimensions to analyze GNSS trajectory data. By designing multiscale grids and temporal partitions, we efficiently processed high-dimensional trajectory data by employing t-SNE and K-means++ algorithms for dimensionality reduction and clustering, and the visualization validated the clustering effectiveness. When applied to GNSS data from the wheat harvest season in China, the results revealed distinct patterns of harvester movement, including trans-regional movement trends. The geogrids are clustered into four groups, each of which exhibits a distinct spatiotemporal relationship. A combined geogrid analysis with administrative regions identified Anhui as having the highest flow density, whereas Henan had the most concentrated areas of trans-regional harvester flow. These findings offer valuable insights for planning harvester operations, particularly in trans-regional harvester management, by understanding complex spatiotemporal dynamics.
Keyword ：
agricultural machinery agricultural machinery cluster analysis cluster analysis geogrid geogrid harvester flow harvester flow Spatiotemporal partition Spatiotemporal partition
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| GB/T 7714 | Zhu, Daoye , Xiao, Boyong , Xie, Haoling et al. Exploring trans-regional harvesting operation patterns based on multi-scale spatiotemporal partition using GNSS trajectory data [J]. | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2025 , 18 (1) . |
| MLA | Zhu, Daoye et al. "Exploring trans-regional harvesting operation patterns based on multi-scale spatiotemporal partition using GNSS trajectory data" . | INTERNATIONAL JOURNAL OF DIGITAL EARTH 18 . 1 (2025) . |
| APA | Zhu, Daoye , Xiao, Boyong , Xie, Haoling , Li, Dong , He, Haitong , Zhai, Weixin . Exploring trans-regional harvesting operation patterns based on multi-scale spatiotemporal partition using GNSS trajectory data . | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2025 , 18 (1) . |
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In addressing global climate change and promoting economic growth, achieving a comprehensive carbon balance at the county level is vital for sustainable development. However, most studies focus on the balance between carbon emissions and sequestration (CESB), neglecting the intricate ecological and economic carbon balance (EECB) at this scale. This study introduces an analytical framework that couples economic development with ecological protection by using coupling coordination degree analysis, carbon balance zoning, and the Markov-PLUS model. Taking Jiangxi Province as a case study, we evaluate the spatio-temporal dynamics of carbon balance from 2000-2020 and predict future trends for 2030 under four potential development scenarios. Results reveal significant regional variations in CESB over the two decades, which primarily exhibit net carbon emission. Meanwhile, the continuous decline in EECB highlights the need for balanced economic and ecological development. By 2030, the land use's carbon sequestration capacity is expected to increase under different scenarios, leading to a 'middle-high, sides-low' spatial pattern in CESB. These findings are crucial for policymakers in devising strategies for sustainable regional development.
Keyword ：
Carbon balance Carbon balance carbon emission carbon emission ecological protection ecological protection economic development economic development land use land use scenario simulation scenario simulation
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| GB/T 7714 | Deng, Yuliang , Huang, Min , Gong, Daohong et al. Carbon balance dynamic evolution and simulation coupling economic development and ecological protection: a case study of Jiangxi Province at county scale from 2000-2030 [J]. | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2025 , 18 (1) . |
| MLA | Deng, Yuliang et al. "Carbon balance dynamic evolution and simulation coupling economic development and ecological protection: a case study of Jiangxi Province at county scale from 2000-2030" . | INTERNATIONAL JOURNAL OF DIGITAL EARTH 18 . 1 (2025) . |
| APA | Deng, Yuliang , Huang, Min , Gong, Daohong , Ge, Yong , Lin, Hui , Zhu, Daoye et al. Carbon balance dynamic evolution and simulation coupling economic development and ecological protection: a case study of Jiangxi Province at county scale from 2000-2030 . | INTERNATIONAL JOURNAL OF DIGITAL EARTH , 2025 , 18 (1) . |
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For objects with arbitrary angles in optical remote sensing (RS) images, the oriented bounding box regression task often faces the problem of ambiguous boundaries between positive and negative samples. The statistical analysis of existing label assignment strategies reveals that anchors with low Intersection over Union (IoU) between ground truth (GT) may also accurately surround the GT after decoding. Therefore, this article proposes an attention-based mean-max balance assignment (AMMBA) strategy, which consists of two parts: mean-max balance assignment (MMBA) strategy and balance feature pyramid with attention (BFPA). MMBA employs the mean-max assignment (MMA) and balance assignment (BA) to dynamically calculate a positive threshold and adaptively match better positive samples for each GT for training. Meanwhile, to meet the need of MMBA for more accurate feature maps, we construct a BFPA module that integrates spatial and scale attention mechanisms to promote global information propagation. Combined with S2ANet, our AMMBA method can effectively achieve state-of-the-art performance, with a precision of 80.91% on the DOTA dataset in a simple plug-and-play fashion. Extensive experiments on three challenging optical RS image datasets (DOTA-v1.0, HRSC, and DIOR-R) further demonstrate the balance between precision and speed in single-stage object detectors. Our AMMBA has enough potential to assist all existing RS models in a simple way to achieve better detection performance. The code is available at https://github.com/promisekoloer/AMMBA.
Keyword ：
Accuracy Accuracy Attention feature fusion Attention feature fusion Detectors Detectors Feature extraction Feature extraction label assignment label assignment Location awareness Location awareness Object detection Object detection optical remote sensing (RS) images optical remote sensing (RS) images Optical scattering Optical scattering oriented object detection oriented object detection Remote sensing Remote sensing Semantics Semantics Shape Shape Training Training
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| GB/T 7714 | Lin, Qifeng , Chen, Nuo , Huang, Haibin et al. Attention-Based Mean-Max Balance Assignment for Oriented Object Detection in Optical Remote Sensing Images [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2025 , 63 . |
| MLA | Lin, Qifeng et al. "Attention-Based Mean-Max Balance Assignment for Oriented Object Detection in Optical Remote Sensing Images" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 63 (2025) . |
| APA | Lin, Qifeng , Chen, Nuo , Huang, Haibin , Zhu, Daoye , Fu, Gang , Chen, Chuanxi et al. Attention-Based Mean-Max Balance Assignment for Oriented Object Detection in Optical Remote Sensing Images . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2025 , 63 . |
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Generally, the interesting objects in aerial images are completely different from objects in nature, and the remote sensing objects in particular tend to be more distinctive in aspect ratio. The existing convolutional networks have equal aspect ratios of the receptive fields, which leads to receptive fields either containing non-relevant information or being unable to fully cover the entire object. To this end, we propose Horizontal and Vertical Convolution, which is a plug-and-play module to address different aspect ratio problems. In our method, we introduce horizontal convolution and vertical convolution to expand the receptive fields in the horizontal and vertical directions, respectively, to reduce redundant receptive fields, so that remote sensing objects with different aspect ratios can achieve better receptive fields coverage, thereby achieving more accurate feature representation. In addition, we design an attention module to dynamically aggregate these two sub-modules to achieve more accurate feature coverage. Extensive experimental results on the DOTA and HRSC2016 datasets show that our HVConv achieves accuracy improvements in diverse detection architectures and obtains SOTA accuracy (mAP score of 77.60% with DOTA single-scale training and mAP score of 81.07% with DOTA multi-scale training). Various ablation studies were conducted as well, which is enough to verify the effectiveness of our model.
Keyword ：
backbone network backbone network irregular aspect ratio irregular aspect ratio object detection object detection redundancy receptive fields redundancy receptive fields
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| GB/T 7714 | Chen, Jinhui , Lin, Qifeng , Huang, Haibin et al. HVConv: Horizontal and Vertical Convolution for Remote Sensing Object Detection [J]. | REMOTE SENSING , 2024 , 16 (11) . |
| MLA | Chen, Jinhui et al. "HVConv: Horizontal and Vertical Convolution for Remote Sensing Object Detection" . | REMOTE SENSING 16 . 11 (2024) . |
| APA | Chen, Jinhui , Lin, Qifeng , Huang, Haibin , Yu, Yuanlong , Zhu, Daoye , Fu, Gang . HVConv: Horizontal and Vertical Convolution for Remote Sensing Object Detection . | REMOTE SENSING , 2024 , 16 (11) . |
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