GeoMaster 行业应用实战指南城市规划、灾害管理与基础设施的地理空间工作流【免费下载链接】scientific-agent-skillsTurn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 190,000 scientists worldwide. 165 ready-to-use validated skills plus 100 scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.项目地址: https://gitcode.com/GitHub_Trending/cl/scientific-agent-skills本指南以 GeoMaster 技能库中的 industry-applications.md 为骨架围绕真实世界的行业地理空间工作流展开城市土地利用分类与人口估算、洪水与野火风险评估、电力走廊与管道选线、交通与公交服务区分析。你将掌握 Sentinel-2 光谱特征 随机森林分类、基于 DEM 的水文建模、多因子风险叠加、最小成本路径等一整套可直接落地的 Python 实现并了解如何复用 SKILL.md 中的 CRS 处理、云掩膜与性能优化实践让代码在真实数据上可运行、可复用。GeoMaster 是面向 GIS、遥感和地球观测的综合性 Agent 技能库覆盖 70 主题、500 代码示例。本文聚焦其中专门讲述行业落地的章节不同行业城市规划、灾害管理、公用事业与基础设施、交通运输如何用同一套矢量/栅格/机器学习栈解决现实问题。所有代码均源自 industry-applications.md并以仓库内其他参考文档与 SKILL.md 中的最佳实践加以注释和补全。环境准备与公共技术底座行业应用代码大量使用geopandas、rasterio、scikit-learn、scipy、networkx、osmnx。按照 SKILL.md 的安装建议# 核心 Python 栈推荐 conda conda install -c conda-forge gdal rasterio fiona shapely pyproj geopandas # 遥感与机器学习 uv pip install rsgislib torchgeo earthengine-api uv pip install scikit-learn xgboost torch-geometric # 网络与可视化 uv pip install osmnx networkx folium keplergl uv pip install cartopy contextily mapclassify # 大数据与云端 uv pip install xarray rioxarray dask-geopandas uv pip install pystac-client planetary-computer多数行业工作流需要输入栅格Sentinel-2 影像、DEM与矢量行政区、道路、基础设施。数据获取可参考>import elevation elevation.clip(bounds(-122.5, 37.7, -122.3, 37.9), outputsrtm.tif) elevation.clean(srtm.tif, srtm_filled.tif)两个贯穿所有案例的注意事项源自 SKILL.mdCRS 一致性进行任何空间运算前先检查坐标系面积/距离计算务必转换到投影坐标系如gdf.estimate_utm_crs()自动检测 UTM。投影坐标系缓冲区、面积、长度计算不要使用 Web MercatorEPSG:3857应使用 UTMEPSG:326xx/327xx。城市规划从土地利用分类到人口估算城市土地利用分类classify_urban_land_use展示了一条标准的监督分类流水线加载训练数据 → 提取光谱与纹理特征 → 随机森林训练 → 整图分类 → 后处理 → 统计。类别为 Residential / Commercial / Industrial / Green Space / Water 五类在注释中约定 0–4 或 1–5 的整数类号。def classify_urban_land_use(sentinel2_path, training_data_path): Urban land use classification workflow. Classes: Residential, Commercial, Industrial, Green Space, Water from sklearn.ensemble import RandomForestClassifier import geopandas as gpd import rasterio # 1. Load training data training gpd.read_file(training_data_path) # 2. Extract spectral and textural features features extract_features(sentinel2_path, training) # 3. Train classifier rf RandomForestClassifier(n_estimators100, max_depth20) rf.fit(features[X], features[y]) # 4. Classify full image classified classify_image(sentinel2_path, rf) # 5. Post-processing cleaned remove_small_objects(classified, min_size100) smoothed majority_filter(cleaned, size3) # 6. Calculate statistics stats calculate_class_statistics(cleaned) return cleaned, stats def extract_features(image_path, training_gdf): Extract spectral and textural features. with rasterio.open(image_path) as src: image src.read() profile src.profile # Spectral features features { NDVI: (image[7] - image[3]) / (image[7] image[3] 1e-8), NDWI: (image[2] - image[7]) / (image[2] image[7] 1e-8), NDBI: (image[10] - image[7]) / (image[10] image[7] 1e-8), UI: (image[10] image[3]) / (image[7] image[2] 1e-8) # Urban Index } # Textural features (GLCM) from skimage.feature import graycomatrix, graycoprops textures {} for band_idx in [3, 7, 10]: # Red, NIR, SWIR band image[band_idx] band_8bit ((band - band.min()) / (band.max() - band.min()) * 255).astype(np.uint8) glcm graycomatrix(band_8bit, distances[1], angles[0], levels256, symmetricTrue) contrast graycoprops(glcm, contrast)[0, 0] homogeneity graycoprops(glcm, homogeneity)[0, 0] textures[fcontrast_{band_idx}] contrast textures[fhomogeneity_{band_idx}] homogeneity # Combine all features # ... (implementation) return features关于代码细节的说明特征提取的索引约定与 SKILL.md 中的光谱指数一致——image[2]B03绿、image[3]B04红、image[7]B08近红外、image[10]B11SWIR1NDVI 用(NIR-Red)/(NIRRed1e-8)1e-8防止除零。四个指数各有物理含义指数公式在土地利用中的作用NDVI(NIR-Red)/(NIRRed)识别植被绿地、农田NDWI(Green-NIR)/(GreenNIR)识别水体NDBI(SWIR-NIR)/(SWIRNIR)识别建筑/不透水面UI城市指数(SWIRRed)/(NIRGreen)增强建成区与裸地对比GLCM 纹理特征对城市异质性高密度建成区、阴影、混合像元尤其重要——纯光谱指数难以区分不同材质的屋顶与道路。后处理中的remove_small_objects(min_size100)与majority_filter(size3)去除孤立像元、平滑斑块边界。完整的分类流程含通过rasterio.features.rasterize从训练矢量提取样本、整图预测并写出classified.tif可参考 SKILL.md 的classify_imagery与 code-examples.md 的第 66 个示例。分类输出可作为下游「人口估算」和「洪水暴露分析」的输入。面插值人口估算Dasymetric Population Redistribution人口普查数据按行政单元如街区汇总与真实人口分布存在偏差。面插值dasymetric mapping用土地利用分类作为辅助数据把总人口按居住适宜性重新分配到网格def dasymetric_population(population_raster, land_use_classified): Dasymetric population redistribution. # 1. Identify inhabitable areas inhabitable_mask ( (land_use_classified ! 0) # Water (land_use_classified ! 4) # Industrial (land_use_classified ! 5) # Roads ) # 2. Assign weights by land use type weights np.zeros_like(land_use_classified, dtypefloat) weights[land_use_classified 1] 1.0 # Residential weights[land_use_classified 2] 0.3 # Commercial weights[land_use_classified 3] 0.5 # Green Space # 3. Calculate weighting layer weighting_layer weights * inhabitable_mask total_weight np.sum(weighting_layer) # 4. Redistribute population total_population np.sum(population_raster) redistributed population_raster * (weighting_layer / total_weight) * total_population return redistributed关键设计排除水体、工业区、道路等不可居住区域对居住区权重 1.0、商业区 0.3、绿地 0.5 进行加权最后按weighting_layer / total_weight归一化确保重新分配的总人口与原始总量守恒np.sum(redistributed) ≈ total_population。这种结果可直接用于公共服务选址、应急资源调度等高精度人口分布场景。灾害管理洪水与野火风险洪水风险评估flood_risk_assessment是「水文建模 → 淹没范围估算 → 暴露分析 → 脆弱性评估 → 风险计算 → 风险图输出」的完整链路def flood_risk_assessment(dem_path, river_path, return_period_years100): Comprehensive flood risk assessment. # 1. Hydrological modeling flow_accumulation calculate_flow_accumulation(dem_path) flow_direction calculate_flow_direction(dem_path) watershed delineate_watershed(dem_path, flow_direction) # 2. Flood extent estimation flood_depth estimate_flood_extent(dem_path, river_path, return_period_years) # 3. Exposure analysis settlements gpd.read_file(settlements.shp) roads gpd.read_file(roads.shp) infrastructure gpd.read_file(infrastructure.shp) exposed_settlements gpd.clip(settlements, flood_extent_polygon) exposed_roads gpd.clip(roads, flood_extent_polygon) # 4. Vulnerability assessment vulnerability assess_vulnerability(exposed_settlements) # 5. Risk calculation risk flood_depth * vulnerability # Risk Hazard × Vulnerability # 6. Generate risk maps create_risk_map(risk, settlements, output_pathflood_risk.tif) return { flood_extent: flood_extent_polygon, exposed_population: calculate_exposed_population(exposed_settlements), risk_zones: risk } def estimate_flood_extent(dem_path, river_path, return_period): Estimate flood extent using Mannings equation and hydraulic modeling. # 1. Get river cross-section # 2. Calculate discharge for return period # 3. Apply Mannings equation for water depth # 4. Create flood raster # Simplified: flat water level with rasterio.open(dem_path) as src: dem src.read(1) profile src.profile # Water level based on return period water_levels {10: 5, 50: 8, 100: 10, 500: 12} water_level water_levels.get(return_period, 10) # Flood extent flood_extent dem water_level return flood_extent原理与实现深度水文建模calculate_flow_direction可参考 scientific-domains.md 的 D8 算法——用 2 的幂编码 8 个流向32/64/128/16/0/1/8/4/2逐像元选取最大落差方向。flow_accumulation与watershed在其基础上累加汇水面积并划分子流域。淹没范围简化模型注释明确指出完整实现应基于曼宁方程Mannings equation与水力模型计算断面流量与水深简化版采用「平水面假设」——根据重现期10/50/100/500 年查表得到水位5/8/10/12 米dem water_level得到淹没掩膜。更精细的做法参见 scientific-domains.md 的flood_inundation加入ndimage.label连通分量过滤只保留 100 像元的水体连通块排除孤立噪声像元并以像元面积如 30m×30m计算淹没总面积code-examples.md 的第 67 个示例则进一步输出淹没水深栅格depth np.where(flooded, flood_level - dem, 0)。暴露与风险用gpd.clip将聚落、道路、基础设施与淹没多边形叠加求交识别暴露资产风险按经典的「风险 危险性 × 脆弱性」risk flood_depth * vulnerability定义最终输出flood_risk.tif风险分级图与暴露人口统计。野火风险建模wildfire_risk_assessment将多源因子相乘构成综合风险场是可解释的乘性风险模型def wildfire_risk_assessment(vegetation_path, dem_path, weather_data, infrastructure_path): Wildfire risk assessment combining multiple factors. # 1. Fuel load (from vegetation) with rasterio.open(vegetation_path) as src: vegetation src.read(1) # Fuel types: 0No fuel, 1Low, 2Medium, 3High fuel_load vegetation.map_classes({1: 0.2, 2: 0.5, 3: 0.8, 4: 1.0}) # 2. Slope (fires spread faster uphill) with rasterio.open(dem_path) as src: dem src.read(1) slope calculate_slope(dem) slope_factor 1 (slope / 90) * 0.5 # Up to 50% increase # 3. Wind influence wind_speed weather_data[wind_speed] wind_direction weather_data[wind_direction] wind_factor 1 (wind_speed / 50) * 0.3 # 4. Vegetation dryness (from NDWI anomaly) dryness calculate_vegetation_dryness(vegetation_path) dryness_factor 1 dryness * 0.4 # 5. Combine factors risk fuel_load * slope_factor * wind_factor * dryness_factor # 6. Identify assets at risk infrastructure gpd.read_file(infrastructure_path) risk_at_infrastructure extract_raster_values_at_points(risk, infrastructure) infrastructure[risk_level] risk_at_infrastructure high_risk_assets infrastructure[infrastructure[risk_level] 0.7] return risk, high_risk_assets四个因子都经过归一化/阈值化处理使结果落在可比较的区间燃料载量按植被类型映射0.2/0.5/0.8/1.0坡度因子1 (slope/90)*0.5将陡坡最多放大 50%火向上坡蔓延更快风速因子1 (wind_speed/50)*0.3体现风助火势干燥度因子基于 NDWI 异常NDWI 的计算见 SKILL.md放大风险最多 40%。坡度计算可用 SKILL.md 的terrain_metricsnp.gradientarctan得到度数坡。最后用extract_raster_values_at_points等价于rasterio.sample.sample_gen见 code-examples.md把风险值落到基础设施点筛选出risk_level 0.7的高风险资产。公用事业与基础设施走廊巡检与管线选线输电走廊植被越界分析power_line_corridor_analysis将矢量缓冲与栅格掩膜结合输出维护优先级图与工单点def power_line_corridor_analysis(power_lines_path, vegetation_height_path, buffer_distance50): Analyze vegetation encroachment on power line corridors. # 1. Load power lines power_lines gpd.read_file(power_lines_path) # 2. Create corridor buffer corridor power_lines.buffer(buffer_distance) # 3. Load vegetation height with rasterio.open(vegetation_height_path) as src: veg_height src.read(1) profile src.profile # 4. Extract vegetation height within corridor veg_within_corridor rasterio.mask.mask(veg_height, corridor.geometry, cropTrue)[0] # 5. Identify encroachment (vegetation safe height) safe_height 10 # meters encroachment veg_within_corridor safe_height # 6. Classify risk zones high_risk encroachment (veg_within_corridor safe_height * 1.5) medium_risk encroachment ~high_risk # 7. Generate maintenance priority map priority np.zeros_like(veg_within_corridor) priority[high_risk] 3 # Urgent priority[medium_risk] 2 # Monitor priority[~encroachment] 1 # Clear # 8. Create work order points from scipy import ndimage labeled, num_features ndimage.label(high_risk) work_orders [] for i in range(1, num_features 1): mask labeled i centroid ndimage.center_of_mass(mask) work_orders.append({ location: centroid, area_ha: np.sum(mask) * 0.0001, # Assuming 1m resolution priority: Urgent }) return priority, work_orders关键点缓冲必须在投影坐标系下进行buffer(buffer_distance)的 50 米距离量纲要求 CRS 为米制投影否则会得到度数缓冲见 SKILL.md 的 CRS 最佳实践。栅格掩膜提取rasterio.mask.mask(veg_height, corridor.geometry, cropTrue)把走廊多边形对应的植被高度裁剪出来code-examples.md 的第 72 个示例展示了同款rasterio.mask.mask用法。风险分级与工单生成安全高度阈值 10 米超过 1.5 倍15 米为 Urgent、其余越界为 Monitor、未越界为 Clear用scipy.ndimage.label对高风险连通区编号逐块取质心生成工单location质心坐标、area_ha面积换算——代码注释假设 1m 分辨率每像元 1m²故×0.0001转为公顷。管道选线最小成本路径optimize_pipeline_route是典型的加权图最短路径应用其骨架数据准备 Dijkstra 路径重建可在真实项目中替换为skimage.graph.MCP_Geometric、gdaltools或pgRouting等实现def optimize_pipeline_route(origin, destination, constraints_path, cost_surface_path): Optimize pipeline route using least-cost path analysis. # 1. Load cost surface with rasterio.open(cost_surface_path) as src: cost src.read(1) profile src.profile # 2. Apply constraints constraints gpd.read_file(constraints_path) no_go_zones constraints[constraints[type] no_go] # Set very high cost for no-go zones for _, zone in no_go_zones.iterrows(): mask rasterize_features(zone.geometry, profile[shape]) cost[mask 0] 999999 # 3. Least-cost path (Dijkstra) from scipy.sparse import csr_matrix from scipy.sparse.csgraph import shortest_path # Convert to graph (8-connected) graph create_graph_from_raster(cost) # Origin and destination nodes orig_node coord_to_node(origin, profile) dest_node coord_to_node(destination, profile) # Find path _, predecessors shortest_path(csgraphgraph, directedTrue, indicesorig_node, return_predecessorsTrue) # Reconstruct path path reconstruct_path(predecessors, dest_node) # 4. Convert path to coordinates route_coords [node_to_coord(node, profile) for node in path] route LineString(route_coords) return route def create_graph_from_raster(cost_raster): Create graph from cost raster for least-cost path. # 8-connected neighbor costs # Implementation depends on library choice pass原理说明成本面cost surface通常由地形坡度、土地类型、穿越成本等多因子叠加而成禁入区no-go zones如保护区、居民区、水体通过栅格化后赋极大成本999999实现「软禁止」。8 连通邻接图每个像元与其 8 个邻居相连边权可取两像元成本的均值正交邻居或乘以 √2对角邻居反映更长距离。Dijkstra 求解scipy.sparse.csgraph.shortest_path(..., return_predecessorsTrue)返回前驱矩阵从终点回溯重建像元序列再经node_to_coord转回地理坐标最终构造成shapely.geometry.LineString。该模式同样适用于道路选线、生态廊道设计、逃生路径规划等「穿越阻力最小」类问题advanced-gis.md 与 specialized-topics.md 对该主题有更多网络分析论述。交通运输流量分析与公交服务区交通流量与拥堵热点分析traffic_analysis把道路网抽象为图用 KNN 空间插值把稀疏的 AADT年平均日交通量观测值推广到全路网def traffic_analysis(roads_gdf, traffic_counts_path): Analyze traffic patterns and congestion. # 1. Load traffic count data counts gpd.read_file(traffic_counts_path) # 2. Interpolate traffic to all roads import networkx as nx # Create road network G nx.Graph() for _, road in roads_gdf.iterrows(): coords list(road.geometry.coords) for i in range(len(coords) - 1): G.add_edge(coords[i], coords[i1], lengthroad.geometry.length, road_idroad.id) # 3. Spatial interpolation of counts from sklearn.neighbors import KNeighborsRegressor count_coords np.array([[p.x, p.y] for p in counts.geometry]) count_values counts[AADT].values knn KNeighborsRegressor(n_neighbors5, weightsdistance) knn.fit(count_coords, count_values) # 4. Predict traffic for all road segments all_coords np.array([[n[0], n[1]] for n in G.nodes()]) predicted_traffic knn.predict(all_coords) # 5. Identify congested segments for i, (u, v) in enumerate(G.edges()): avg_traffic (predicted_traffic[list(G.nodes()).index(u)] predicted_traffic[list(G.nodes()).index(v)]) / 2 capacity G[u][v][capacity] # Need capacity data G[u][v][v_c_ratio] avg_traffic / capacity # 6. Congestion hotspots congested_edges [(u, v) for u, v, d in G.edges(dataTrue) if d.get(v_c_ratio, 0) 0.9] return G, congested_edges实现要点图建模沿道路几何的每对相邻顶点建立带length与road_id的边更工程化的做法是用 SKILL.md 的osmnx.graph_from_place()直接下载带属性限速、通行时间的路网配合ox.add_edge_speeds()/ox.add_edge_travel_times()计算真实出行时间。空间插值KNeighborsRegressor(n_neighbors5, weightsdistance)用距离反比加权推断无观测路段的流量若只有坐标而无路网也可用 code-examples.md 的sklearn.neighbors.BallTree做最近邻查询。V/C 比路段流量V与通行能力C之比是衡量拥堵的国际通行指标v_c_ratio 0.9视为拥堵热点代码注释提示需要额外准备容量字段capacity。公交服务区分析transit_service_area回答「步行 X 分钟内能到达哪些区域」这一公交规划经典问题def transit_service_area(stops_gdf, max_walk_distance800, max_time30): Calculate transit service area considering walk distance and travel time. # 1. Walkable area around stops walk_buffer stops_gdf.buffer(max_walk_distance) # 2. Load road network for walk time roads gpd.read_file(roads.shp) G osmnx.graph_from_gdf(roads) # 3. For each stop, calculate accessible area within walk time service_areas [] for _, stop in stops_gdf.iterrows(): # Find nearest node stop_node ox.distance.nearest_nodes(G, stop.geometry.x, stop.geometry.y) # Get subgraph within walk time walk_speed 5 / 3.6 # km/h to m/s max_nodes int(max_time * 60 * walk_speed / 20) # Assuming ~20m per edge subgraph nx.ego_graph(G, stop_node, radiusmax_nodes) # Create polygon from reachable nodes reachable_nodes ox.graph_to_gdfs(subgraph, edgesFalse) service_area reachable_nodes.geometry.unary_union.convex_hull service_areas.append({ stop_id: stop.stop_id, service_area: service_area, area_km2: service_area.area / 1e6 }) return service_areas方法要点双重可达性先做 800 米直线缓冲stops_gdf.buffer(800)需投影 CRS再做基于路网的实际步行可达计算两者结合更贴近真实步行路径。步速换算默认步行速度 5 km/h即 1.39 m/smax_nodes用总步行时间 × 步速 / 每边平均长度假设 ~20m/边粗略换算成网络半径nx.ego_graph取以站点节点为中心的可达子图。服务区面积可达节点集合的凸包作为服务区多边形输出area_km2面积除以 1e6。ox.distance.nearest_nodes与ox.graph_to_gdfs来自 osmnx与 SKILL.md 的网络分析一脉相承。行业工作流中的共性最佳实践以上五个行业场景虽然领域不同但共享同一套工程规范源自 SKILL.md任何空间操作前校验 CRSassert gdf1.crs gdf2.crs面积/距离/缓冲一律用投影坐标系。栅格大文件按块处理for i, window in src.block_windows(1)逐块读取或dask.array.from_rasterio惰性计算见 SKILL.md。云掩膜先行光学影像如 Sentinel-2应先做云掩膜再计算 NDVI 等指数SKILL.md 的 STAC 流程可用 SCL 波段或eo:cloud_cover 20过滤云量。几何校验与缺失处理gdf gdf[gdf.is_valid]、gdf[geometry].fillna(None)。效率优先的格式GeoPackage 优于 Shapefile大批量数据用 Parquet / Arrowgdf.to_file(..., use_arrowTrue)GDAL 缓存gdal.SetCacheMax(2**30)可显著加速栅格 I/O。可复现性为每个工作流保存数据版本、CRS、参数与随机种子保留数据血缘。进一步阅读industry-applications.md本文的原始骨架含全部代码与更多行业变体code-examples.md — 500 示例覆盖分类、淹没制图、地形分析、栅格裁剪/合并/重投影等底层操作scientific-domains.md — D8 流向算法、淹没建模、农业、林业等学科工作流data-sources.md — Sentinel/Landsat/DEM/土地覆盖数据目录与 API 访问SKILL.md — 安装、核心概念CRS、OGC 标准、光谱指数、云原生工作流与性能调优advanced-gis.md 与 specialized-topics.md — 网络分析、最优化与专题深化的延伸主题本文中的每个工作流都可作为独立基线替换为本地真实数据Sentinel-2 L2A 产品、SRTM/Copernicus DEM、OSM 路网与 POI即可复用在 SKILL.md 的 500 示例与性能指南辅助下扩展为生产级地理空间分析管线。【免费下载链接】scientific-agent-skillsTurn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 190,000 scientists worldwide. 165 ready-to-use validated skills plus 100 scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard.项目地址: https://gitcode.com/GitHub_Trending/cl/scientific-agent-skills创作声明：本文部分内容由AI辅助生成（AIGC），仅供参考