A topography-aware approach to the automatic generation of urban road networks

Existing deep-learning tools for road network generation have limited applications in flat urban areas due to their overreliance on the geometric and spatial configurations of street networks and inadequate considerations of topographic information. This paper proposes a new method of street network generation based on a generative adversarial network by designing a pre-positioned geo-extractor module and a geo-merging bypath. The two improvements employ the complementary use of geometric configurations and topographic features to automate street network generation in both flat and hilly urban areas. Our experiments demonstrate that the improved model yields a more realistic prediction of street configurations than conventional image inpainting techniques. The model’s effectiveness is further enhanced when generating streets in hilly areas. Furthermore, the geo-extractor module provides insights from the computer vision perspective in recognizing when topographic information should be considered and which topographic information should receive more attention.</p

Additional file 4 of Characteristics of the nasal mucosa of commercial pigs during normal development

Additional file 4. The distribution of lymphoid follicles in the nasal respiratory region of pigs at different ages. Representative images of HE-stained nasal respiratory regions from pigs at different ages, including 7 days old (A), 60 days old (B) and 180 days old (C). The red frame in each figure indicates the different parts of the inferior nasal concha (a and b), and magnified images of the corresponding region are shown on the right of the figure. Black asterisks mark the lymphoid follicle. CSII: the anterior part of the respiratory region; CSIII: the rear part of the respiratory region; Scale bars: (A to C) 2 mm; (a, b) 200 μm

Additional file 6 of Characteristics of the nasal mucosa of commercial pigs during normal development

Additional file 6. OTU-based community composition and diversity analysis. (A) Venn diagram showing the shared and unique OTUs in nasal swab samples collected from different age groups. Based on the OTU composition, the biodiversity of the samples (alpha diversity) from the different age groups was calculated with the Shannon (B), Sob (C) and Chao1 (D) indices. The beta diversity of bacterial communities from the different age groups is shown in the PCoA plot

Additional file 5 of Characteristics of the nasal mucosa of commercial pigs during normal development

Additional file 5. HE staining of the olfactory region of the nasal cavity in different growth stages. (A) Diagrams of pig nasal cavity cross-section IV (corresponding to the olfactory region). (B-F) Representative images of HE-stained nasal olfactory regions from pigs at different ages, including 0 days old (B), 7 days old (C), 30 days old (D), 60 days old (E), and 180 days old (F). The red frame in each figure indicates the middle nasal concha (a) and nasal septum (b); magnified images of the corresponding region are shown on the right of the figure. Scale bars: (B-F) 2 mm; (a, b) 50 μm

Additional file 3 of Characteristics of the nasal mucosa of commercial pigs during normal development

Additional file 3. Histological analysis of the four cross-sections of the porcine nasal cavity. (A) Quantitative analysis of the epithelial thickness of the four regions of the porcine nasal cavity. The epithelial thickness of the nasal mucosa was measured using ImageJ software, and five visual fields (40 ×) were randomly selected from the five individual sections. All the measurements summarized in the column plot are provided as the average epithelial thickness per group. (B) Quantitative analysis of the number of glands in the lamina propria of the four regions of the porcine nasal cavity. The number of glands was counted in five randomly selected visual fields (10 ×) of the five individual sections, summarized in a column plot. (C) Quantitative analysis of the number of capillaries in the four regions of the nasal cavity. The number of capillaries in the nasal mucosa was counted in five randomly selected visual fields (10 ×) of the five individual sections. All data shown are the mean ± SD from three independent experiments. Statistical significance was obtained using one-way ANOVA. The differences are indicated by different letters. Letters above the graphs indicate statistical significance in which treatments with a letter in common are not significantly different from each other

Additional file 7 of Characteristics of the nasal mucosa of commercial pigs during normal development

Additional file 7. Quantitative analysis of the total area of the nasal concha in the nasal respiratory and olfactoria regions. The area of the nasal concha was measured using ImageJ software, and the results were obtained from five individual sections. The histogram shows the mean ± SD for the area of middle or inferior turbinates of the nasal cavity at different growth stages. Data are representative of three independent experiments. The differences are indicated by different letters. Letters above the graphs indicate statistical significance in which treatments with a letter in common are not significantly different from each other

Additional file 2 of Characteristics of the nasal mucosa of commercial pigs during normal development

Additional file 2. HE staining of the vestibular region of the nasal cavity in different growth stages. (A) Diagrams of pig nasal cavity cross-Section I (corresponding to the vestibular region). (B-F) Representative images of HE-stained nasal vestibular regions from pigs at different ages, including 0 days old (B), 7 days old (C), 30 days old (D), 60 days old (E), and 180 days old (F). The red frame in each figure indicates the inferior nasal concha (a) and nasal septum (b); magnified images of the corresponding region are shown on the right of the figure. Scale bars: (B-F) 2 mm; (a, b) 50 μm

Additional file 1 of Characteristics of the nasal mucosa of commercial pigs during normal development

Additional file 1. Primers used for real-time PCR