MalaGIS

Sharing GIS Technologies, Resources and News.

During a casual chat in the MalaGIS WeChat group, someone asked when GIS skills become most useful in daily life. Answers ranged from food delivery route optimization to travel planning, but the most insightful response was "assessing location (including Feng Shui) for property purchases". Among factors like sunlight access—a critical yet often overlooked parameter—most standard map apps fail to provide this data, requiring specialized GIS software (refer to methods in "ArcGIS 3D Handbook: Sunlight and 3D Path Analysis"). Recently, I discovered an online WebGIS application that simulates sunlight and shadows for any global location at any date—an excellent tool worth sharing.

Official site: https://shademap.app/

ShadeMap enables browser-based simulation of mountain, building, and tree shadows at any global coordinate for customizable dates/times. Users achieve precise, interactive sunlight and shadow analysis without client installations.

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Remote sensing imagery is crucial for global monitoring but often limited by sensor spatial resolution and the high cost of acquiring ultra-high-resolution data. The Sentinel-2 (S2) mission provides multispectral imagery across 13 bands at 10m, 20m, and 60m resolutions. However, these resolutions may not capture fine details required for tasks like land cover mapping, agricultural monitoring, or disaster assessment. Super-Resolution (SR) technology addresses this by reconstructing high-resolution images from low-resolution inputs, significantly enhancing spatial detail in S2 imagery for more precise data support.

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Another major GIS company appears to have encountered security issues. The incident occurred on July 25th. While casually browsing the Spicy GIS group chat, our editor noticed a member sharing a link with the caption "XX got hacked." Initially, the editor didn't pay much attention, assuming it was just another prank like the "Crazy Thursday send me 50" meme. About a week later, on August 2nd, the editor accidentally clicked that link and was surprised to discover that the website genuinely seemed to have been compromised.

Admittedly, the initial discovery was shocking—this is one of China's top-tier GIS companies after all. It highlights that security concerns should be a priority for organizations regardless of size; even the largest companies can have critical oversights.

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In a previous article "Maptalks: An Open-Source Alternative to Cesium for Geospatial Visualization", we introduced maptalks as an alternative framework to Cesium. Recently, we discovered another open-source 2D/3D engine: three-tile. Designed for lightweight implementation with ongoing functional improvements, it already meets significant development demands. Here is a brief introduction.

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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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In the previous article "Discover Landsat's New Tool: Turning Your Name into Artistic Maps", I introduced NASA's fun application "Your Name In Landsat". Recently, the official website became inaccessible, prompting inquiries from readers about alternatives. After searching without finding a viable solution, I decided to recreate the experience myself.

Access the demo: https://malagis.com/extension/demo/your-name-in-landsat/

Below is a brief overview of the implementation process.

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As professionals in the GIS (Geographic Information System) field, we engage with geographical data, spatial analysis, and visualization technologies daily. Recently, I discovered an impressive website called "No Days Off" on HackerNews. Developed by friggeri using 10 years of daily running GPX files, this site functions as a sophisticated "personal running GIS system," showcasing remarkable professionalism and engagement. I'm fascinated by such innovative projects and would like to share its details.

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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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