I paused the video…
Stared at it…
You can do that?!

TLDR: I searched a STAC Catalog for Sentinel-2 and Landsat imagery, accessed their COG assets and programmatically built a QGIS project that streams false-colour composites - no (persistent) download required.

Steps to stream a Cloud-Optimised GeoTIFF (COG) in QGIS

1. Open QGIS

2. Go to Layer → Add Layer → Add Raster Layer …

3. In the dialog:

   1. Source type: Protocol: HTTP(S), cloud, etc.

   2. Type: HTTP/HTTPS/FPT

   3. URI: https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/36/R/VU/2025/3/S2C_36RVU_20250313_0_L2A/TCI.tif

4. Click Add

Or, using AWS S3 Protocol (works for the same URI once we separate the S3 Bucket name and Object key)

3. In the dialog:

   1. Source type: Protocol: HTTP(S), cloud, etc.

   2. Type: AWS S3

   3. Bucket or container: sentinel-cogs

   4. Object key: sentinel-s2-l2a-cogs/36/R/VU/2025/3/S2C_36RVU_20250313_0_L2A/TCI.tif

4. Click Add

And then I wondered…

Could I do this programmatically?

Could I:

• Query a STAC catalog for relevant imagery,

• Select the COG assets I care about and

• Build a QGIS project that streams these COGs — on-the-fly?

Turns out… yes.

I’m not sure whether this is relevant to others, or if people have similar use cases, but I often find myself:

• Quickly browsing satellite images when starting a new project

• Overlaying ML model outputs or vector masks on imagery to visually inspect them

• Or simply loading multiple scenes to compare change over time

Traditionally, that means downloading scenes, unzipping folders, organizing files, and manually dragging them into QGIS.

Now I can build a QGIS project with a script, giving me a much better starting point, with the key layers already in place.

Head over to the Jupyter Notebook to try it out for yourself → streaming-COGs-in-QGIS.ipynb

Users can now access the EnMAP data archive! [link]

People have been curious about EnMAP images since its launch in April and are very keen to explore this new and rich data source. It is only a matter of time before the first applications and use cases start coming out, showcasing EnMAP’s value and great potential! We have already had a peek; 
Makhtesh Ramon (Israel) — EnMAP records the largest erosion cirque in the world, 
EnMAP images from the Lower Lausitz — An East-German region in transition.

Since early last week, people have been able to sign up to EnMAP’s Instrument Planning platform and access the archive. Follow the rest of the article for instructions on how to download your first EnMAP image!

First, take a look at the Portals User Manual; 
5.6 Available User Roles for the role descriptions and at 6. Step by Step Manual for the EnMAP Data Access and Ordering Process for instructions on how to register, request a role assignment and search the EnMAP data archive.

Register at https://planning.enmap.org/ips/

Next, view the GIF below for how to search through the EnMAP data archive, select order options and place your order! (click on the GIF to view on full-screen)
NOTE: You don’t have to draw a polygon on the map; this way, you will get all available results.

Although it is stated that “Archived data can be accessed immediately free of charge” it took about 30 hours for my order to move from ‘SUBMITTED’ → ‘IN PROCESSING’ → ‘PROCESSED’. 
I guess a lot of people have been downloading EnMAP data these days!

Finally, once your order is processed, you will receive an email with the address of the FTP Delivery Server and the path to the data (e.g. ftps://xen0f0n-cat1distributor@download.dsda.dlr.de//dims_op_oc_oc-en_700329792_1.tar.gz)

Follow this guide for a list of FTP clients to download your order. 
Alternatively, you can use wget to do the same from the command line.

wget — user {username}-cat1distributor — password {password} {ftps_link}

Researchers just got an early Christmas present… and it has 230 spectral channels!

In a recent paper, titled “EarthNets: Empowering AI in Earth Observation”, Xiong et al. put together a queryable collection of 401 Remote Sensing datasets (featured in the latest Spectral Reflectance Newsletter). As part of that, they also developed Dataset4EO — a Python library that provides an easy-to-use way of loading datasets. Having this as motivation I tried three different Python libraries we can use to download about 80 different Remote Sensing datasets; radiant_mlhub, torchgeo and dataset4eo.

These three Python libraries try to accomplish slightly different things; 
radiant_mlhub.Dataset provides access to 64 datasets for users to download locally, while dataset4eo and torchgeo go one step further; Both provide a PyTorch DataLoader for each of the available datasets. So, instead of having users write their own custom PyTorch Dataset/DataLoader classes, they can use pre-built ones. By doing so, users would have a streamlined way of downloading a dataset and feeding their models. Cool, right?

Radiant MLHub

The radiant_mlhub library just provides the means to download datasets, without any fancy DataLoader wrappers on top of it or Deep Learning models shipped with it.
After getting an API key things are fairly straightforward. Here you can have a look at the 64 available datasets through radiant_mlhub. In the following code blocks there are instruction on how to request an API key, list the available datasets and download the CV4A Kenya Crop Type Competition dataset.

Torchgeo

Torchgeo is a PyTorch domain library focused on geospatial data that provides both datasets and (pre-trained) models. 
One of the great things about torchgeo is that there are available PyTorch DataLoaders for the datasets; meaning that once you download a dataset you can use an out-of-the-box DataLoader to train your model (in PyTorch)! However, in some cases torchgeo doesn’t provide a “download method” and expects that users have already downloaded the dataset. In addition, to download some datasets you need to have a Radiant MLHub API key, as a few of them are fetched from there. Here is a list of the datasets available through TorchGeo. 
Once again, using torchgeo datasets is simple. Have a look at the code below for how to download the same dataset as before and iterate through a PyTorch DataLoader.

NOTE: At the moment of this writing, TorchGeo only supports radiant-mlhub 0.2.1–0.4. Versions 0.5+ won’t work! [link]

To learn more about TorchGeo, here is their paper on arxiv.

Dataset4EO

The EarthNets platform (part of which is dataset4eo) is designed to provide an easy way to apply (deep learning) models to Remote Sensing datasets. Furthermore, EarthNets pushes for a more consistent evaluation of deep learning methods on remote sensing data by selecting 5 different datasets as benchmarks on 3 different tasks; image classification, object detection ad semantic segmentation. As the authors state, “there are some difficulties in loading RS datasets, especially for researchers in other communities” and “it would be helpful to download, decompress and split the dataset [into train/val/test] automatically”.
The are several aspects of dataset4eo that make it very similar to TorchGeo; both provide a PyTorch (or PyTorch compatible) DataLoader and deep learning models to use out-of-the-box.
Here, however, we are more interested in the “dataset loading” part of it.

At the moment, I’m afraid that dataset4eo isn’t a matured enough codebase. While trying to run the code example and download the LandSlide4Sense dataset I was faced with several errors. Eventually I managed to run the LandSlide4Sense example and iterate through a PyTorch DataLoader. Check this ticket here for the steps I followed to work around the errors I was getting.

In the table below, I have put together a short comparison among the three libraries.