MalaGIS

Sharing GIS Technologies, Resources and News.

pyroSAR is an open-source Python framework designed for large-scale Synthetic Aperture Radar (SAR) satellite data processing. Its core objective is to provide a complete and scalable solution for SAR data organization, processing, and analysis, integrating data acquisition, metadata management, preprocessing, and interaction with mainstream processing software like ESA SNAP. This significantly simplifies complex SAR data processing workflows.

Official Website: https://github.com/johntruckenbrodt/pyroSAR/

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Three months ago, during preparations for a leadership inspection of our WebGIS dashboard project (arguably its most critical application), our project manager urgently contacted me the night before: "XXX, emergency! The basemap's peripheral elements on the GIS dashboard have disappeared—only data remains visible!"

Reluctantly accessing the system, I discovered the dynamic visualizations had vanished. Console errors revealed resource loading failures traced to our CDN service. Checking my personal CDN account (used due to small company scale), I found payment overdue—promptly recharging 200 CNY.

A month later, while debugging new features, CDN errors recurred. Initially attributing this to post-exhibition traffic spikes (even boasting about "high system usage" to my manager), I recharged another 200 CNY.

When another billing alert arrived just weeks later—despite the exhibition ending months prior—abnormal traffic patterns became undeniable.

Initial Investigation

Qiniu Cloud's backend revealed alarming patterns:

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In previous articles, such as "Using Aria2 to Download GIS Data" and "Recommended Websites for Downloading Earthquake Data", the USGS (United States Geological Survey) was introduced as a source for global earthquake data. However, this website has a limitation: it will reject download requests if the query exceeds 2000 records. Therefore, a practical workaround is to construct download requests by month and download the data in batches. If I wanted to download all the data from 1900 until now, manually creating each download link would be very tedious. Hence, I came up with the following solution.

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When working with large raster datasets in QGIS, especially Float64 GeoTIFFs with hundreds of millions of cells, you may want to locate and zoom into the pixel that holds the maximum value. This can be useful in terrain analysis, remote sensing, or any context where the peak value matters.

A common approach involves converting the raster into a point or polygon layer. However, this is resource-intensive and often impractical for large datasets. This tutorial introduces an efficient alternative using PyQGIS and NumPy to directly zoom to the maximum value without raster reclassification or vectorization.

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Previously I recommended historical GIS resources include:

• AllHistory (Perfect World's feature-rich platform with polished UI)
• Taiwan-maintained Chinese Historical WMTS Service (China-exclusive basemaps)

Recently discovered OldMapsOnline - a globally-oriented historical mapping solution.

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QGIS provides two methods for creating geometries using expressions: Geometry Generator (introduced in QGIS 2.14) and Geometry by Expression (introduced in QGIS 3.0). Both leverage QGIS's expression engine but serve fundamentally different purposes. This tutorial clarifies their differences and appropriate use cases.

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In a previous article "Geo Hound: A Tool to Automatically Fetch Website Map Service Addresses", I introduced the powerful Geo Hound tool. Follow-up articles like "Get Hidden Government Map Data in 2 Steps (Geo Hound + Wuhan One Map)" and "Shanghai One Map Data (Trial)" shared practical experiences. Many readers subsequently asked: Can this map data be downloaded for offline use? While various methods exist, I'll share my two most-used approaches.

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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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Extracting river/canal centerlines is a common task in GIS data processing. Users of ArcMap or ArcGIS Pro likely find it straightforward. Recently, many colleagues have started exploring QGIS, someone in My GIS Group asked about this, making it a good time to document the process.

PS: The author will focus on introducing methods to achieve ArcGIS-equivalent functions in QGIS. If you have specific needs in this area, feel free to leave a comment.

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In a previous article "Practical Application of Geo Hound: Extracting Wuhan's Unified Planning Map Services for GIS Analysis", I demonstrated using Geo Hound to access Wuhan's planning map data in QGIS. A key objective was comparing land-use planning attributes against reality to identify discrepancies. While I mentioned using layer swipe for this comparison, many readers inquired about implementing swipe functionality in QGIS.

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