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学者姓名：朱道也
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Abstract ：
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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Abstract ：
Urban open spaces provides various benefits to citizens, but the thermal environment under this space is being affected by the accelerated urbanization and global warming. Based on this, this paper is dedicated to conducting research on improving the attractiveness of outdoor environmental spaces and improving outdoor thermal comfort. The main work of this paper is first to propose a street comfort model by considering both environmental and climatic factors, which is trained to learn using indirect data. Secondly, the comfort level of each street is combined with the frequency of non-motorized trips on that street to obtain the urgency index of rectification for that street and to achieve accurate recommendations for urban planning. Considering the public accessibility of the data in the paper in cities across China, this study can be easily deployed to other cities to support urban planning and provide useful recommendations for improvement of urban open spaces. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Keyword ：
Indirect learning Indirect learning Street comfort model Street comfort model Street rectification urgency index Street rectification urgency index Street solar radiation value Street solar radiation value
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| GB/T 7714 | Xie, L. , Yu, Z. , Huang, F. et al. Recommendations for Urban Planning Based on Non-motorized Travel Data and Street Comfort [未知]. |
| MLA | Xie, L. et al. "Recommendations for Urban Planning Based on Non-motorized Travel Data and Street Comfort" [未知]. |
| APA | Xie, L. , Yu, Z. , Huang, F. , Zhu, D. . Recommendations for Urban Planning Based on Non-motorized Travel Data and Street Comfort [未知]. |
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Abstract ：
Remote sensing data have become an important data source for urban and regional change detection, owing to their advantages of authenticity, objectivity, immediacy, and low cost. The method of collection and management for remote sensing change detection samples (RS_CDS) assumes a crucial role in the effectiveness of remote sensing intelligent change detection (RSICD). To achieve rapid collection and real-time sharing of RS_CDS, this study proposes a grid collection and management model of RS_CDS based on GeoSOT (GCAM-GeoSOT), including the grid collection method of RS_CDS (GCM-SD) and grid management method of RS_CDS (GMM-SD). To verify the feasibility and retrieval efficiency of GMM-SD, Oracle and PostgreSQL databases were combined and the retrieval efficiency and database capacity were compared with the corresponding spatial databases, Oracle Spatial and PostgreSQL + PostGIS, respectively. The experimental results showed that GMM-SD not only ensures the reasonable capacity consumption of the database but also has a higher retrieval efficiency for the RS_CDS. This results in a noteworthy comprehensive performance enhancement, with a 47.63% improvement compared to Oracle Spatial and a 40.24% improvement compared to PostgreSQL + PostGIS.
Keyword ：
GeoSOT GeoSOT grid collection grid collection grid management grid management remote sensing change detection sample remote sensing change detection sample
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| GB/T 7714 | Zhu, Daoye , Han, Bing , Silva, Elisabete A. et al. Novel Grid Collection and Management Model of Remote Sensing Change Detection Samples [J]. | REMOTE SENSING , 2023 , 15 (23) . |
| MLA | Zhu, Daoye et al. "Novel Grid Collection and Management Model of Remote Sensing Change Detection Samples" . | REMOTE SENSING 15 . 23 (2023) . |
| APA | Zhu, Daoye , Han, Bing , Silva, Elisabete A. , Li, Shuang , Huang, Min , Ren, Fuhu et al. Novel Grid Collection and Management Model of Remote Sensing Change Detection Samples . | REMOTE SENSING , 2023 , 15 (23) . |
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