Article
Computer Science, Interdisciplinary Applications
Wenliang Li
Summary: Detailed urban land-use patterns are crucial for urban management, economic analysis, and policy-making towards sustainable urban development. This study proposed a framework combining social sensing data and remote sensing images to map the land-use patterns of New York City, achieving high classification accuracy that can be applied in urban planning and building energy use modeling.
EARTH SCIENCE INFORMATICS
(2021)
Article
Environmental Sciences
Ying Tu, Bin Chen, Wei Lang, Tingting Chen, Miao Li, Tao Zhang, Bing Xu
Summary: Detailed information on urban land uses has been an essential requirement for urban land management and policymaking. Recent advances in remote sensing and machine learning technologies have contributed to the mapping and monitoring of multi-scale urban land uses, yet there lacks a holistic mapping framework that is compatible with different end users' demands. Our proposed framework offered an alternative to investigating urban land use composition, which could be applied in a broad range of implications in future urban studies.
Article
Remote Sensing
Guangqin He, Guolin Cai, Yongshu Li, Taiyun Xia, Zheng Li
Summary: In this study, a ULU classification method based on weighted split-flow network and hierarchical multitasking was proposed. By optimizing the learning ability and enhancing model robustness, the accuracy of ULU classification in high-resolution remote sensing images was effectively improved.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2022)
Article
Environmental Sciences
Yuan Yao, Yee Leung, Tung Fung, Zhenfeng Shao, Jie Lu, Deyu Meng, Hanchi Ying, Yu Zhou
Summary: This paper analyzes the differences between traditional remote sensing data and continuous multi-angle remote sensing (CMARS) data, highlighting the advantages of using CMARS data for classification. Real-life experiments show the superiority of CMARS data over traditional data in classification, with an increase in overall accuracy of up to about 9%. The research also explores the advantages and disadvantages of utilizing CMARS data directly and the potential for better utilization through the extraction of key features characterizing spectral reflectance variations.
Article
Environmental Sciences
Jie Yu, Peng Zeng, Yaying Yu, Hongwei Yu, Liang Huang, Dongbo Zhou
Summary: The classification of urban land-use information is crucial for applications such as urban planning and administration. This paper presents a combined convolutional neural network named DUA-Net for complex and diverse urban land-use classification. By using GIS data, a well-tagged and high-resolution urban land-use image dataset is created, and the DUA-Net effectively fuses multi-source semantic information using channel attention. The proposed method achieves high-precision urban land-use classification, which is valuable for urban planning and national land resource surveying.
Article
Environmental Sciences
Shuangtao Wang, Pingping Luo, Chengyi Xu, Wei Zhu, Zhe Cao, Steven Ly
Summary: Reconstruction of historical land uses and analysis of modern land-use data showed an increase in urban construction land area, a decrease in unused land and water bodies over the past 435 years in Xi'an. The increase in urban green space and buildings, along with factors such as impervious surfaces, building density, and water areas, contribute to exacerbating urban flooding risks in the study area.
Article
Environmental Studies
Nesru H. Koroso
Summary: Ethiopia's urban land lease policy aims to facilitate the transfer of land for various purposes. However, there is little knowledge about its effectiveness in promoting sustainable urban land use. This paper examines the effects of the lease policies on urban land use efficiency through remote sensing data and analysis of satellite imagery. The study findings reveal low urban land use efficiency, indicating the ineffectiveness of existing land institutions.
Article
Chemistry, Multidisciplinary
Josept David Revuelta-Acosta, Edna Suhail Guerrero-Luis, Jose Eduardo Terrazas-Rodriguez, Cristian Gomez-Rodriguez, Gerardo Alcala Perea
Summary: This study provides the most recent analysis of land use and land cover change in Coatzacoalcos, Mexico over the past six years. The study utilized remote sensing technology and ground-based surveys to analyze the changes. The findings indicate a slowdown in growth for residential, industry, and commercial areas, as well as significant degradation of swamps. Dunes and areas with high vegetation density transitioned to low vegetation density areas.
APPLIED SCIENCES-BASEL
(2022)
Review
Remote Sensing
Jiadi Yin, Jinwei Dong, Nicholas A. S. Hamm, Zhichao Li, Jianghao Wang, Hanfa Xing, Ping Fu
Summary: Remote Sensing has been used in urban mapping for a long time, however, the complexity and diversity of urban functional patterns are difficult to be captured by RS only. Emerging Geospatial Big Data are considered as the supplement to RS data, and help to contribute to our understanding of urban lands from physical aspects to socioeconomic aspects. The integration of RS and GBD features were categorized into feature-level integration and decision-level integration in urban land use classification.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Remote Sensing
Yunkun Bai, Guangmin Sun, Yu Li, Peifeng Ma, Gang Li, Yuanzhi Zhang
Summary: Optical features show stronger capabilities in LULC classification, with key features like NDVI and GLCM's Mean value carrying distinct information for discriminating certain land-use types. The complementary mechanism of optical and polarimetric SAR features can be observed in the radar chart.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Remote Sensing
Jiangfan Feng, Wei Zheng, Zhujun Gu, Dongen Guo, Rui Qin
Summary: This article introduces a specific attention-based network called PaANet for semantic segmentation of remote sensing images. By incorporating position-aware attention and pyramid pooling expectation-maximization modules, this method significantly improves recognition accuracy and the continuity of ground object recognition while preserving structural classification details. The research also proposes a multiresolution data augmentation method that further enhances the model's performance and generalization ability.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2023)
Article
Green & Sustainable Science & Technology
Zhiwen Yang, Hebing Zhang, Xiaoxuan Lyu, Weibing Du
Summary: The study of high-precision land-use classification is crucial for sustainable land resource development. This study proposes a new framework that combines active and passive remote-sensing data to improve the accuracy of land-use classification in cities with high surface humidity. The results demonstrate the effectiveness of the proposed deep-learning classification model.
Article
Multidisciplinary Sciences
Yang Ju, Iryna Dronova, Xavier Delclos-Alio
Summary: This study focuses on the mapping of urban green space (UGS), highlighting the limited efforts dedicated to UGS mapping. By applying supervised classification to Sentinel-2 satellite images and UGS samples from OpenStreetMap, the first 10m resolution UGS map for major urban clusters in Latin America was successfully produced. The resulting map allows for the measurement of UGS area, spatial configuration, and human exposures, facilitating research on the relationship between UGS and environmental hazards, public health outcomes, urban ecology, and urban planning.
Article
Computer Science, Hardware & Architecture
S. Ansith, A. A. Bini
Summary: The development of new deep learning algorithms has significantly changed land use classification. Recent models combine deep neural network structures with machine learning algorithms for feature extraction and classification. The proposed model based on the modified GAN architecture can achieve better results with fewer training samples, making it superior to other deep learning models.
Article
Environmental Sciences
Qingli Li, Xingwei Ren, Jin Luo
Summary: This paper conducted a detailed assessment of water infiltration of the urban surface in Wuhan, China based on remote sensing. Classification of land-use types and their water infiltration criterion was proposed, and the water infiltration capacity distribution was classified into five levels. These results will provide a basis for establishing a comprehensive water infiltration model of the urban surface in the future.
ENVIRONMENTAL EARTH SCIENCES
(2021)
