SIRIUS - Synthesized Inventory of CRitical Infrastructure and HUman-Impacted Areas in Permafrost Regions of AlaSka

The SIRIUS inventory integrates data from (i) the Sentinel-1/2 derived Arctic coastal human impact dataset (SACHI) (Bartsch et al., 2021), (ii) OpenStreetMap dataset for the infrastructure and land use information (OpenStreetMap Contributors and Geofabrik GmbH, 2018), (iii) the pan-Arctic catchments summary database (ARCADE) for the watersheds (Speetjens et al., 2022), (iv) the modeled Northern Hemisphere permafrost map by Obu et al. (2018), and (v) the contaminated sites database and reports by the State of Alaska Department of Environmental Conservation (2023) (DEC) to create a unified new dataset of critical infrastructure and human-impacted areas as well as permafrost and watershed information for Alaska. The dataset is deployed as a GeoPackage and can be imported to spatial databases (e.g. PostgreSQL/PostGIS), a Geographic Information System (e.g. QGIS), and used within geospatial processing libraries (e.g. Python's GeoPandas). All layers can be queried either in dependence or combination with one another. Each GeoPackage contains the following layers: ARCADE_WatershedsDB DEC_ContaminatedSitesAK OSM_Point_InfrastructureHIElements SACHI_OSM_InfrastructureHIElements SACHI_OSM_InfrastructureHIElements_RRNetwork UiO_MAGT UiO_PermafrostProbability UiO_PermafrostZones A corresponding manuscript, including application examples and a thorough description of the individual components, was submitted to be published in an open-access journal. <strong>Download Data</strong> <strong>Python Scripts</strong> 01_InfrastructureDataETL: reprojects the input Shapefiles and raster datasets to a common coordinate system (EPSG:5936) and then clips datasets to the boundary of Alaska. It also includes a step for filtering the permafrost probability raster dataset based on a minimum probability threshold of 50% and rounds the values in the mean annual ground temperature raster dataset. 02_OSM-aggregation: processes the OpenStreetMap (OSM) geospatial data. It imports and merges OSM polygon and point data, cleans and extracts unique values of "fclass" and "osm_type", and aggregates these values for manual categorization, based on the OSM key-value-scheme. The script assigns Land Use/Cover Area frame Statistical Survey (LUCAS) categories to the data, filters out natural objects and places, and resolves unknown categories by identifying intersections between datasets. 03_SACHI-aggregation: assigns LUCAS categories to the SACHI dataset based on the 'Use' column. 04_SACHI-OSM_decisiontree: performs a series of geospatial operations to determine the overlap between polygonal OSM features and SACHI features and assigns LUCAS categories to the overlapping features based on certain criteria and dissolves them. The overlapping and non-overlapping features are then combined into a single dataset: the harmonized critical infrastructure and human-impacted areas dataset. 05_TextMiningNLTK-CSSites: performs text mining and data preprocessing on the reports of the DEC contaminated sites database. It extracts dates, calculates cleanup times for inactive sites, identifies contaminants based on abbreviations and text entries, and extracts information related to contaminants and the medium they are found in. <strong>GeoPackages</strong> PermaRisk_RRNetworkLine_v01_r00.gpkg contains the rail and road network as line geometries. PermaRisk_RRNetworkPolygonal_v01_r00.gpkg contains the rail and road network as polygon geometries.SIRIUS 数据集整合了五类数据：(i) 基于 Sentinel-1/2 生成的北极海岸人类影响数据集（简称 SACHI，Bartsch 等，2021）；(ii) 用于获取基础设施与土地利用信息的开放街道地图（OpenStreetMap，简称 OSM）数据集（OpenStreetMap 贡献者与 Geofabrik GmbH，2018）；(iii) 用于流域信息的泛北极流域汇总数据库（简称 ARCADE，Speetjens 等，2022）；(iv) Obu 等人（2018）构建的北半球多年冻土模拟地图；(v) 阿拉斯加州环境保护局（State of Alaska Department of Environmental Conservation，简称 DEC，2023）发布的污染场地数据库与报告，最终生成一套针对阿拉斯加地区的统一新型数据集，涵盖关键基础设施、受人类活动影响区域、多年冻土与流域相关信息。 该数据集以 GeoPackage 格式发布，可导入空间数据库（如 PostgreSQL/PostGIS）、地理信息系统（如 QGIS），并可配合地理空间处理库（如 Python 的 GeoPandas）使用。所有图层既可独立查询，也可组合查询。每个 GeoPackage 包含以下图层：ARCADE_WatershedsDB、DEC_ContaminatedSitesAK、OSM_Point_InfrastructureHIElements、SACHI_OSM_InfrastructureHIElements、SACHI_OSM_InfrastructureHIElements_RRNetwork、UiO_MAGT、UiO_PermafrostProbability、UiO_PermafrostZones。 相关研究论文（含应用案例与各组件详细说明）已投稿至开放获取期刊。<strong>数据下载</strong> <strong>Python 脚本</strong> 01_InfrastructureDataETL：将输入的 Shapefile 与栅格数据集重投影至统一坐标系（EPSG:5936），随后将所有数据集裁剪至阿拉斯加边界。该流程还包含基于 50% 的最小概率阈值对多年冻土概率栅格数据集进行筛选，以及对年平均地温栅格数据集的数值进行取整的步骤。 02_OSM-aggregation：处理开放街道地图（OSM）的地理空间数据。该脚本导入并合并 OSM 的多边形与点数据，清洗并提取“fclass”与“osm_type”字段的唯一值，基于 OSM 键值对规则对这些值进行手动分类聚合。脚本会为数据分配土地利用/覆盖区域框架统计调查（Land Use/Cover Area frame Statistical Survey，简称 LUCAS）类别，过滤掉自然地物与场所，并通过识别数据集间的交集来解决未知分类问题。 03_SACHI-aggregation：基于“Use”字段为 SACHI 数据集分配 LUCAS 类别。 04_SACHI-OSM_decisiontree：执行一系列地理空间操作，以确定 OSM 多边形要素与 SACHI 要素的重叠区域，并基于特定规则为重叠要素分配 LUCAS 类别，随后将其融合。最终将重叠与非重叠要素合并为单一数据集：协调统一后的关键基础设施与受人类活动影响区域数据集。 05_TextMiningNLTK-CSSites：对阿拉斯加州环境保护局污染场地数据库的报告进行文本挖掘与数据预处理。该流程提取日期信息，计算非活跃场地的清理时长，基于缩写与文本条目识别污染物，并提取与污染物及其赋存介质相关的信息。 <strong>GeoPackage 数据集</strong> PermaRisk_RRNetworkLine_v01_r00.gpkg 包含以线几何形式存储的铁路与公路路网。 PermaRisk_RRNetworkPolygonal_v01_r00.gpkg 包含以多边形几何形式存储的铁路与公路路网。