MalaGIS

Sharing GIS Technologies, Resources and News.

As a GIS frontend developer, I work extensively with various large-scale graphics libraries: OpenLayers, Leaflet, Mapbox GL JS, Cesium, Turf.js... These libraries often contain tens of thousands of lines of code. I wonder if you've encountered situations during development where you needed to modify the source code within node_modules. For example, in my old projects using OpenLayers 6.x, some third-party plugins were no longer updated. In such cases, upgrading to newer plugin versions would require upgrading the OpenLayers version, which is often an impossible workload for a project delivered years ago.

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Just a few days ago, the MapLibre community officially released the MapLibre Tile (MLT) format. This could very well be the most fundamental and hardcore technological innovation in the WebGIS domain since Mapbox defined the MVT standard a decade ago. The official announcement claims that MLT can achieve compression rates up to 6 times better than MVT and decoding speeds 3 times faster! So what exactly is MLT? Why is it positioned to challenge MVT? Let's delve into the details today.

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In previous articles such as "Revisiting Locating User Positions Using IP Addresses (ip2region)" and "Summary of Methods for Locating User Positions Using IP Addresses", we introduced many methods and tools for IP geolocation. These include GeoIP, ipip, ip2region, as well as numerous third-party service providers like Amap and Baidu. Among them, some are online, some are offline, some are paid, and some are free. A few days ago, the editor came across another interesting open-source IP geolocation library: public-ip-address.

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During GIS project delivery, situations with weak or no network connectivity are frequently encountered. Typically, we need to manually download map tiles from various providers. Currently, there are numerous map downloaders available on the market, both free and paid. Upon closer thought, this process isn't particularly complex; it essentially breaks down into three steps: defining the area of interest, downloading all tiles within that area, and exporting. Therefore, while working on a project, the author searched for a simple, small-scale downloader and ultimately found one on GitHub: OfflineMapDownloader.

Project Introduction

Original Project Repository:

https://github.com/0015/OfflineMapDownloader

After testing, the author identified several significant issues, primarily in the following three areas:

1. It only supports OpenStreetMap and ArcGIS Online.
2. It does not support downloading tiles for regions in China.
3. The downloaded .mbtiles files cannot be opened in QGIS.

The issue with China regions was particularly critical, rendering the tool unusable for the author. Consequently, the author made some simple fixes and created a new version. The author's version repository:

https://github.com/sailor103/OfflineMapDownloader

This version primarily addresses the three aforementioned problems. It can now correctly export maps for Chinese regions and supports opening the files in QGIS, as shown below:

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In today's era of booming real-world 3D technology, OSGB format models are widely used. However, "how to publish efficiently and browse smoothly" has always been a pain point for developers: slow loading, large file sizes, laggy interactions on mobile devices... Even star engines like Cesium struggle with issues such as "bloated architecture and subpar mobile experience." In mid-January this year, Zhongke Tuxin open-sourced a lightweight real-world 3D model publishing engine — Wish3D. It claims to be free and open-source, accessible for both individuals and enterprises without barriers. The editor saw it in the MalagiGIS group yesterday and immediately downloaded it for a test drive. Overall, it's indeed fast, but there are several minor issues. Let's share the experience today.

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Recently, while working on an offline map deployment project, I found that the transfer efficiency was relatively low and offline deployment cumbersome when dealing with massive amounts of scattered tile files. Initially, I considered using a ZIP package for transfer and extraction, but later discovered that a format already existed which implements this concept and does so even better: MBTiles. MBTiles is a map tile storage specification based on an SQLite database. It packages thousands of independent map tiles (PNG, JPG, or vector PBF) into a single .mbtiles file.

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On January 5, 2026, Mapbox, a leading global location services platform, officially announced the launch of its Mapbox 3D Lanes feature, designed to address the common problem of taking wrong turns at complex intersections. The Mapbox 3D Lanes feature includes lane geometry, lane markings, and 3D models of overpasses and tunnels, helping drivers follow their routes more easily.

How Effective Is It?

Frankly, our team was immediately captivated by the released preview images. After all, when it comes to aesthetic appeal, Mapbox has never been one to shy away from a challenge.

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In a previous article titled "Introducing GlobalBuildingAtlas: A Global Building Height Dataset ", I shared a global building dataset that includes height information. However, some readers noted concerns about its data quality. Therefore, I would like to share two other global building footprint datasets I discovered while working on CIM-related projects: one from Microsoft (open-source) and another from Google. Both are products of major tech companies, and their usefulness can be evaluated through testing.

Microsoft GlobalMLBuildingFootprints

Microsoft's GlobalMLBuildingFootprints open-source repository provides a global building footprint dataset. It detects approximately 1.4 billion buildings from multi-source Bing Maps imagery (2014–2024) and is freely available under the ODbL license. The data is provided in row-separated GeoJSON format (with a .csv.gz extension) using the EPSG:4326 coordinate system and includes attributes such as estimated height and confidence scores.

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A few days ago, while casually browsing, I heard experts in a group chat mention that NVIDIA has officially opened access to the GLM-4.7 and minimax-m2.1 models for free use. For someone like me, who even refuses to pay a 10 RMB repositioning fee for shared bikes, this immediately caught my attention! After a day of intermittent practical testing for GIS documentation writing and GIS development, while these models aren't as good as top-tier paid ones yet, they are quite adequate for daily GIS tasks. If you're currently on a tight budget and your daily GIS development work isn't too complex, you might want to give them a try. Given the current GIS market and economic climate, every bit of savings helps.

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In recent years, terms featuring the word "cloud" have become incredibly popular, from public cloud and private cloud to cloud-native, cloud-edge integration, and even national land cloud, planning cloud, etc. It seems almost every industry is moving to the cloud. But have you ever wondered, when we talk about so-called "cloud" data, are those streams of 1s and 0s literally traveling through the sky from a physical perspective?

Actually, no. In fact, over 90% of global international data traffic is transmitted through cables lying in the dark depths of the ocean. From the short video you just watched, to cross-border financial transactions, and even the answers you get from Gemini or ChatGPT, are all exchanged via lengthy submarine cables. But where exactly are they laid? Which countries are international communication hubs? How do different cables differ in bandwidth and operators? Today, let me recommend a must-bookmark professional tool website for all GISers — Submarine Cable Map.

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