Figure - available from: Journal of Geovisualization and Spatial Analysis
This content is subject to copyright. Terms and conditions apply.
Study area

Study area

Source publication
Article
Full-text available
In this study, we investigated the accuracy of surface models and orthophoto mosaics generated from images acquired using different data acquisition methods at different processing levels in two urban study areas with different characteristics. Experimental investigations employed single- and double-grid flight directions with nadir and tilted (60°...

Similar publications

Article
Full-text available
The rapid development of uncrewed aerial vehicles (UAVs) has significantly increased their usefulness in various fields, particularly in remote sensing. This paper provides a comprehensive review of UAV path planning, obstacle detection, and avoidance methods, with a focus on its utilisation in both single and multiple UAV platforms. The paper clas...

Citations

... In modern society, with the continuous intensification of urbanization, as a core component of urban ecosystems, the planning, protection, and rational utilization of urban green spaces (UGSs) take a critical part in maintaining ecological balance, improving the life quality of urban residents and urban environment (Kowe et al. 2021;Nagy et al. 2024). UGSs not only provide places for leisure and entertainment but also play an irreplaceable role in regulating the climate, purifying the air, lessening noise, and beautifying the environment (Sufiyan et al. 2022). ...
Article
Full-text available
With the intensification of urbanization, urban green spaces are of great significance for maintaining ecological balance. However, traditional methods for extracting green space information have problems such as insufficient accuracy and slow processing speed. Therefore, the study constructed a dataset using high-resolution remote sensing images collected by drones. A visual geometric group network structure U-shaped network model for remote sensing green space information extraction was proposed, and an improved activation function was designed. The research model was experimentally validated. The experiment outcomes showed that the proposed model had a fast convergence speed, converging after 10 iterations with a loss value of only 0.024, an overall accuracy of 98.54%, and an average Kappa coefficient of 0.921. In practical application testing, the proposed model showed excellent performance, prominent effect, minimal information loss, and overall precision remained above 97%. In addition, the green space information extraction rate of the proposed model exceeded 95.00%, with an average of 96.97%, far superior to traditional methods. This research provides new technological means for monitoring and managing urban green spaces, which helps promote the growth of remote sensing information extraction technology and is significant for urban planning, ecological environment protection, and sustainable development.
... Flood susceptibility assessment is an important initiative to identify flood-prone areas, improve emergency preparedness, and reduce disaster risk (Nagy et al., 2024;Shokouhifar et al., 2022;Tayyab et al., 2024). In this study, we evaluate the flood-prone areas in Fuzhou City by developing a novel integrated machine learning method, i.e., LG-MLP-LR. ...
Article
Full-text available
Flood susceptibility assessment is the premise and foundation to prevent flood disaster events effectively. To accurately assess urban flood susceptibility (UFS), this study first analyzes the advantages and disadvantages of multi-layer perceptron (MLP), and light gradient boosting machine (LightGBM), and designs a new integrated machine learning method by combining logistic regression (LR) method, i.e., LG-MLP-LR. Then, we verify the performance of LG-MLP-LR by taking the flood disaster events in Fuzhou from 2013 to 2016 as example and analyze the contribution of flood conditioning factors by calculating the SHapley Additive exPlanations values. Finally, the assessment results are compared with MLP, LightGBM, XG-MLP-LR, and CB-MLP-LR. The results show that (1) the selected flood conditioning factors can accurately depict the UFS of the study area; (2) compared with MLP, LightGBM, XG-MLP-LR, and CB-MLP-LR, the assessment results by LG-MLP-LR have higher average accuracy (94.950%) and higher average AUC (98.813%); (3) the factors affecting the occurrence and damage degree of flood disaster events in Fuzhou from 2013 to 2016 were elevation, topographic wetness index, maximum one-day rainfall, and stream power index, respectively. This study provides a new idea and method for the effective prevention and control of flood disasters in cities.