MalaGIS

Sharing GIS Technologies, Resources and News.

On September 10, 2025, the National Forestry and Grassland Administration (NFGA) announced Notice No. 12 of 2025, officially approving the establishment of the Huangyan Island National Nature Reserve. In accordance with the relevant provisions of the "Regulations of the People's Republic of China on Nature Reserves," the area, scope, and functional zoning of the Huangyan Island National Nature Reserve are hereby published. The reserve is located in Sansha City, Hainan Province, covering a total area of 3,523.67 hectares. This includes a core zone of 1,242.55 hectares and an experimental zone of 2,281.12 hectares. The primary conservation objective is the coral reef ecosystem. The main areas are shown in the figure below:

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Recently, Nano Banana has been explosively popular, with many novel use cases emerging online, such as generating figurines, image restoration, and architectural image generation models. Attracted by these, we also tried it out and discovered two small applications related to GIS. Today, we'd like to share the prompts with you (Code is Cheap, show me the talk).

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Google has officially launched AlphaEarth Foundations, a PB-scale AI model for integrating satellite data, accompanied by a groundbreaking 64-dimensional Satellite Embedding Dataset. This innovation distills multi-year, multi-satellite observations into a single 10m × 10m pixel, compressing dozens of data sources – including satellite imagery, radar, elevation, and climate data – into unified 64-dimensional "information capsules". This fundamentally redefines geospatial analysis methodologies.

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Recently in the Mala GIS group, someone raised an interesting question: Can GIS be used to analyze which province in China has produced the most leaders? Previously, you might have had to search via a search engine, but the data might not be up-to-date, and quantitative analysis would be cumbersome, requiring tedious data cleaning. However, in the AI era, we can easily accomplish this task using AI + GIS.

P.S.: This article is purely technical, and the data may not be entirely accurate. Corrections are welcome if any issues are found.

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QGIS 3.44 has now been officially released. I downloaded and tried it out immediately. For Windows users, QGIS 3.44 offers both a test version with Qt6 support ("Latest Version for Windows (3.44) with Qt6 (experimental)") and the standard 3.44 version ("Latest Version for Windows (3.44)").

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In a previous article, I introduced a streamlined workflow using DeepSeek + QGIS (Beyond the Hype: Practical Integration of DeepSeek in GIS Workflows). It's undeniable that large language models like ChatGPT and DeepSeek are profoundly transforming the GIS industry. Recently, the domestic AI editor Trae officially launched. After testing it, I'm convinced this tool is essential—it significantly accelerates workflows for both GIS development and data processing!

What is Trae?

If you're unfamiliar with Trae, you might recognize Cursor; if not Cursor, perhaps Windsurf? If those don't ring a bell, have you encountered Cline, CodeGPT, v0, Bolt.new, Tongyi Lingma, or Douban MarsCode? If none sound familiar, it's time to catch up with AI advancements.

Simply put, Trae is a competitor to Cursor—the pioneering AI editor that redefined coding. Unlike autocomplete plugins (e.g., GitHub Copilot), AI IDEs like Trae and Cursor enable comprehensive code modification, refactoring, documentation, and commenting. They support multi-model integration, provide repository-wide intelligence, debug in real-time, and offer interactive AI conversations like DeepSeek.

While I previously used Cursor, its unstable performance in China limited my recommendations. But with DeepSeek's open-source release and localized optimizations, ByteDance's Trae delivers exceptional quality—highly recommended!

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From late last year to early this year, DeepSeek has undoubtedly dominated the tech landscape. My social feeds—public accounts and short videos—have featured daily updates about DeepSeek, from model comparisons to website glitches and workarounds for full-featured access. Concurrently, industries and institutions have launched an "arms race," with corporate announcements flooding newsfeeds about xxx adopting DeepSeek. Sectors like internet services, finance, construction, and even government agencies are rushing to deploy DeepSeek solutions, as if falling behind would mean obsolescence. The GIS industry is no exception—software vendors and GIS practitioners alike have publicly announced their DeepSeek integrations.

This prompts a question for GIS professionals like myself: After integrating DeepSeek, what comes next? Where are the practical applications for DeepSeek + GIS?

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Valentine’s Day is here again, and the GIS community group at Malagis is buzzing with excitement. Watching the lively discussions makes one nostalgic for youthful enthusiasm. On such a romantic day, shouldn't everything—including GIS—have its own charm? Let’s explore two GIS-inspired romantic ideas shared by the group.

1. The Bonne Projection

Do you still remember the Bonne projection? Or have your cartography lessons faded into memory? Here's what it looks like:

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When visualizing national highway mileage across Chinese provinces in QGIS, I needed provincial capital markers without local data access. Using DeepSeek AI, I generated geographic layers directly in QGIS via Python scripting.

Visualization requiring provincial capitals

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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.

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