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ucam-eo
tessera-interactive-map
Python2025–2025
ucam-eo / tessera-interactive-map

tessera-interactive-map

Interactive land cover classification of TESSERA embeddings

2Distinct people
3Mentions
0Explicit recommenders
58GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Interactive land cover classification of TESSERA embeddings

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:03:16 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by hour3 dated mentions

Observed mentions: 2025-09-25 20:45:17 UTC to 2025-09-26 08:37:50 UTC.

0122025-09-25 20:00:00 UTC to 2025-09-25 21:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 commentsSep 25, 20:002025-09-25 21:00:00 UTC to 2025-09-25 22:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-25 22:00:00 UTC to 2025-09-25 23:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-25 23:00:00 UTC to 2025-09-26 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-26 00:00:00 UTC to 2025-09-26 01:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-26 01:00:00 UTC to 2025-09-26 02:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-26 02:00:00 UTC to 2025-09-26 03:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsSep 26, 02:002025-09-26 03:00:00 UTC to 2025-09-26 04:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-26 04:00:00 UTC to 2025-09-26 05:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-26 05:00:00 UTC to 2025-09-26 06:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-26 06:00:00 UTC to 2025-09-26 07:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-26 07:00:00 UTC to 2025-09-26 08:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-26 08:00:00 UTC to 2025-09-26 09:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsSep 26, 08:00

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Sep 25, 20:002025-09-25 20:00:00 UTC2025-09-25 21:00:00 UTC202
Sep 25, 21:002025-09-25 21:00:00 UTC2025-09-25 22:00:00 UTC000
Sep 25, 22:002025-09-25 22:00:00 UTC2025-09-25 23:00:00 UTC000
Sep 25, 23:002025-09-25 23:00:00 UTC2025-09-26 00:00:00 UTC000
Sep 26, 00:002025-09-26 00:00:00 UTC2025-09-26 01:00:00 UTC000
Sep 26, 01:002025-09-26 01:00:00 UTC2025-09-26 02:00:00 UTC000
Sep 26, 02:002025-09-26 02:00:00 UTC2025-09-26 03:00:00 UTC000
Sep 26, 03:002025-09-26 03:00:00 UTC2025-09-26 04:00:00 UTC000
Sep 26, 04:002025-09-26 04:00:00 UTC2025-09-26 05:00:00 UTC000
Sep 26, 05:002025-09-26 05:00:00 UTC2025-09-26 06:00:00 UTC000
Sep 26, 06:002025-09-26 06:00:00 UTC2025-09-26 07:00:00 UTC000
Sep 26, 07:002025-09-26 07:00:00 UTC2025-09-26 08:00:00 UTC000
Sep 26, 08:002025-09-26 08:00:00 UTC2025-09-26 09:00:00 UTC101

What Hacker News says

3 sources
I would try https://github.com/ucam-eo/tessera-interactive-map , this is relatively easy to get started with and has a nice interface for labeling. https://github.com/ucam-eo/geotessera has an image showing our embedding coverage at the moment. Blue areas we have complete coverage for 2024, green areas we cover 2017-2024. We're slowly trying to populate everything 2017-2024 but the constraint is GPU and storage at the moment - each year takes ~20k GPU/200k CPU hours and requires storing and serving 200 terabytes of data. The world is big! If there is an area you would like prioritised, there's an issue template on the geotessera github repo which we can use to move regions around in the processing queue.
In Can a model trained on satellite data really find brambles on the ground?
That's should be a pretty good usecase; if you do just a few labels manually of known hotsprings you should be able to find others quite quickly using the TESSERA interactive notebook. The embeddings capture the annual spectral-temporal signature, so a hotspring should be fairly distinctive vs the surroundings. Video of the notebook in action https://crank.recoil.org/w/mDzPQ8vW7mkLjdmWsW8vpQ and the source https://github.com/ucam-eo/tessera-interactive-map
In Can a model trained on satellite data really find brambles on the ground?
Yes it's very good fun just exploring the embeddings! It's all wrapped by the geotessera Python library, so with uv and gdal installed just try this for your favourite region to get a false-colour map of the 128-dimensional embeddings: # for cambridge # https://github.com/ucam-eo/geotessera/blob/main/example/CB.geojson curl -OL https://raw.githubusercontent.com/ucam-eo/geotessera/refs/heads/main/example/CB.ge… # download the embeddings as geotiffs uvx geotessera download --region-file CB.geojson -o cb2 # do a false colour PCA down to 3 dimensions from 128 uvx geotessera visualize cb2 cb2.tif # project onto webmercator and visualise using leafletjs over openstreetmap uvx geotessera webmap cb2.tif --output cb2-map --serve Because the embeddings are precomputed, the library just has to download the tiles from our serve
Read the rest
r. More at: https://anil.recoil.org/notes/geotessera-python Downstream classifiers are really fast to train (seconds for small regions). You can try out a notebook in VSCode to mess around with it graphically using https://github.com/ucam-eo/tessera-interactive-map The berries were a bit sour, summer is sadly over here!
In Can a model trained on satellite data really find brambles on the ground?

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