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Mountain cities are characterized by undulating terrain, complex road networks, and diverse road facilities, which makes accessing food more difficult than in cities with a flat terrain. This study proposes an enhanced two-step method based on the Baidu map service for the construction of supermarket–market–retail food sales architecture and for calculating food accessibility. The accessibility indices of seven major food categories (grains and oils, fruits, vegetables, seafood, meat, milk, and eggs) were calculated considering the principle of the fairest walking routes in Chongqing. The correlations between food accessibility and house price and house age in Chongqing were explored through local Moran’s analysis and geographically weighted regression. The correlations illustrated the fairness of the distribution of food accessibility in Chongqing among the poor and rich. The experiments showed generally well-developed food accessibility in the main urban areas of Chongqing. However, accessibility to fresh fruits and vegetables lagged in newly built urban areas.

Abstract. This research assessed the influences of four band combinations and three types of pretrained weights on the performance of semantic segmentation in extracting refugee dwelling footprints of the Kule refugee camp in Ethiopia during a dry season and a wet season from very high spatial resolution imagery. We chose a classical network, U-Net with VGG16 as a backbone, for all segmentation experiments. The selected band combinations include 1) RGBN (Red, Green, Blue, and Near Infrared), 2) RGB, 3) RGN, and 4) RNB. The three types of pretrained weights are 1) randomly initialized weights, 2) pretrained weights from ImageNet, and 3) weights pretrained on data from the Bria refugee camp in the Central African Republic). The results turn out that three-band combinations outperform RGBN bands across all types of weights and seasons. Replacing the B or G band with the N band can improve the performance in extracting dwellings during the wet season but cannot bring improvement to the dry season in general. Pretrained weights from ImageNet achieve the best performance. Weights pretrained on data from the Bria refugee camp produced the lowest IoU and Recall values.