When crafting dynamic marathon routes from official maps, directly tracing simplified route images is inefficient without proper spatial referencing. Georeferencing solves this by aligning raster images with real-world coordinates.
When crafting dynamic marathon routes from official maps, directly tracing simplified route images is inefficient without proper spatial referencing. Georeferencing solves this by aligning raster images with real-world coordinates.
When processing DWG data in QGIS, exporting multiple layers individually becomes tedious. Here are three efficient batch export methods。
When asked about generating dynamic marathon route maps like those in Beijing Marathon 2024, I discovered MapPlus—a zero-code solution for creating animated race visualizations. This tool eliminates the need for custom development while producing professional-grade outputs.
In our previous article 《「GIS Data」Download Updated National Administrative Division Codes》, we discussed methods to acquire administrative codes. A common application is powering location selection components in frontend systems, as shown below:
When our frontend team requested updated administrative division codes, I leveraged the 2024 national standard vector map dataset (Approval No. GS(2024) 0650) shared in our previous article. This official dataset contains accurate administrative codes within its attribute tables, making it ideal for extraction.
Building upon our previous sharing of district/county-level administrative division data, we now provide the 2024 updated version sourced from China's official platform. This dataset addresses limitations in earlier versions (e.g., data gaps, insufficient detail) while maintaining cartographic compliance.
Following our previous article on the https://malagis.com/gis-data-share-2010-2020-global-forest-age-distribution-gami-data.html, this guide demonstrates netCDF visualization techniques in QGIS, eliminating the need for specialized tools like IDL.
TorchGeo is a PyTorch domain library, similar to torchvision, specifically designed for geospatial data. It provides datasets, samplers, transforms, and pretrained models to help machine learning practitioners work with geospatial data and enable remote sensing experts to explore machine learning solutions.
When tasked with downloading decades of monthly GIS data from a restricted website, I encountered aggressive CAPTCHA challenges that blocked batch downloads. As a lazy GIS professional unwilling to perform repetitive manual downloads, I developed an automated solution.
Amidst China's strategic focus on secure and controllable information technology systems, the domestic innovation ecosystem has ushered in unprecedented opportunities. The rise of homegrown operating systems, databases, middleware, and GIS software has fortified information security while providing new technological foundations for the GIS industry. Within this context, innovations in GIS cartography transcend traditional spatial visualization, advancing toward intelligent, personalized, and real-time applications that unlock new possibilities for creating high-value maps.
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