Urban Built Environment Assessment Based on Scene Understanding of High-Resolution Remote Sensing Imagery

Abstract

A high-quality built environment is important for human health and well-being. Assessing the quality of the urban built environment can provide planners and managers with decision-making for urban renewal to improve resident satisfaction. Many studies evaluate the built environment from the perspective of street scenes, but it is difficult for street-view data to cover every area of the built environment and its update frequency is low, which cannot meet the requirement of built-environment assessment under rapid urban development. Earth-observation data have the advantages of wide coverage, high update frequency, and good availability. This paper proposes an intelligent evaluation method for urban built environments based on scene understanding of high-resolution remote-sensing images. It contributes not only the assessment criteria for the built environment in remote-sensing images from the perspective of visual cognition but also an image-caption dataset applicable to urban-built-environment assessment. The results show that the proposed deep-learning-driven method can provide a feasible paradigm for representing high-resolution remote-sensing image scenes and large-scale urban-built-area assessment.

Scale Effect of Land Cover Classification from Multi-Resolution Satellite Remote Sensing Data

“…The classification results are better for the small-scale resolution of feature types with larger areas and single features; for instance, grassland is better classified on 10 m and 30 m images with less pepper and classification accuracy of GBDT is higher than that of RF, which indicates that the effect of boosting ensemble classification is better than that of bagging ensemble classification. The classification accuracy of the two ensemble classification methods was the highest at the spatial resolution of 4 m and 6 m and decreased when the image resolution exceeded or was smaller than 4 m. In terms of classification accuracy analyses, the optimal spatial scale of land cover for both study areas is 4-6 m, which is consistent with related studies [11,42]. classification accuracy of GBDT is higher than that of RF, which indicates that the effect of boosting ensemble classification is better than that of bagging ensemble classification.…”

“…The classification accuracy of GBDT is higher than that of RF, which indicates that the effect of boosting ensemble classification is better than that of bagging ensemble classification. The classification accuracy of the two ensemble classification methods was the highest at the spatial resolution of 4 m and 6 m and decreased when the image resolution exceeded or was smaller than 4 m. In terms of classification accuracy analyses, the optimal spatial scale of land cover for both study areas is 4-6 m, which is consistent with related studies [11,42].…”

Interactive Change-Aware Transformer Network for Remote Sensing Image Change Captioning

“…Remote sensing image captioning (RSIC) aims to generate sentences that describe the contents of the given RS image with natural language. Recently, most of the RSIC works [10,[13][14][15][16][17][18][19][20][21] have used deep learning techniques and adopted an encoder-decoder framework for caption generation. The visual encoder utilizes a CNN [12] or a Vision Transformer [22] pre-trained network to extract the visual features from the input image, then injects the features into the RNN-based [23] or Transformer-based [8] decoder to generate the descriptive sentences.…”