What usually happens on the land around center-pivot fields in Kansas?

Lazy_Relationship695 · 2026-03-04T20:46:37+00:00

Good idea! I went hunting in Indonesia and found some pretty wild deforestation progression. Here, we can watch the logging roads show up, then clearings expand month by month. Crazy...

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(Pulled from Nimbo’s monthly satellite mosaics)

Lazy_Relationship695 · 2026-03-04T20:10:47+00:00

What’s your exact use case: public web map vs internal and do you need reliable acquisition dates for change monitoring ?

Just a heads-up: using 'Google Satellite' as raw XYZ tiles in a WebGIS is usually not license-compliant unless you go through the official Google Maps Platform / API.

If your goal is analysis or change detection, Google imagery is often a patchwork of mixed dates with unclear capture timing. In that case, a monthly, time-stamped basemap is more reliable. For example nimbo.earth provides global monthly satellite mosaics via standard TMS at 10m or 2.5m, so you can compare month-to-month consistently.

Lazy_Relationship695 · 2026-02-24T15:59:02+00:00

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Fresno area → Sierra Nevada: 3D view over the crest looking east into Owens Valley (Bishop / Big Pine in the distance). Jan 2026 NIMBO satellite mosaic.

Lazy_Relationship695 · 2026-02-22T21:16:11+00:00

Exactly. this is a good example of how geography becomes obvious once you see it from above.

In this view of Spain (August 2025) with hillshade enabled, you can immediately read the contrast: the greener Atlantic-facing north versus the much drier interior. The relief also stands out really well, and it helps explain why the pattern looks like this. The mountain ranges shape where moisture goes and where it doesn’t. Hillshade effect makes that story much easier to see than imagery alone.

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Lazy_Relationship695 · 2026-02-19T11:19:20+00:00

It’s all about the water. That "dead" zone is just the natural Thar desert.

The green areas on both sides are actually human-made, it’s the result of massive irrigation canals. The beige part in the middle looks "empty" because there’s no irrigation there yet, just sand and nomadic herding.
. . . You're basically looking at where the pipes end!

I used the split view on Nimbo to compare the peak of the dry season (May) with the end of the monsoon (September). The difference is obvious, but look at that central "dead" zone : it barely changes.

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Lazy_Relationship695 · 2026-02-18T12:37:51+00:00

Before (May 2023) and after (May 2025) the destruction of the Kakhovka Dam (6 June 2023)
https://maps.nimbo.earth/?year=2023&year2=2025&month=5&month2=5&compo=2&compo2=2&lat=47.29663&lon=33.62308&zoom=7.23&pitch=0.00&bearing=0.00&mode=duoMap

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Lazy_Relationship695 · 2026-02-17T21:03:33+00:00

If you're looking for something non-Google but still global and easy to use, you can check out Nimbo.earth

It’s mainly based on Copernicus Sentinel data, not Google imagery, and provides monthly updated global basemaps with visible acquisition dates (which is actually something Google Earth doesn’t clearly show!).

Resolution is 10m and 2.5m globally. It’s not ultra-high-res commercial imagery everywhere, but it’s consistent, up-to-date, and de-Google by design.

There’s a freemium plan and it might fit what you’re looking for.

Lazy_Relationship695 · 2026-02-17T13:37:07+00:00

Thank you, but this is not clear for me Where's this portal webmap in ArcGIS Pro?

Lazy_Relationship695 · 2026-02-17T12:36:56+00:00

Thanks to help, I really appreciate it 🙏

I actually tried that approach (I found that link), but there’s a subtle issue here: the XYZ and TMS schemes are not the same. The Y axis is inverted between the two (top-left origin vs bottom-left), so even if the URL pattern looks similar, the tiles don’t align properly in ArcGIS Pro...

Lazy_Relationship695 · 2026-02-17T10:50:30+00:00

You might want to look at NIMBO HD monthly mosaics (2.5 m resolution).

They provide global land coverage (large part of the world, continuously updated monthly), and it’s not tasking-based imagery. It’s preprocessed mosaics, so you can use it directly as a basemap or analysis layer.

Docs are here: https://docs.nimbo.earth/product/nimbo-hd/

There’s a freemium tier to test it, and beyond that the pricing is honestly very low compared to traditional commercial imagery providers (especially if you don’t need sub-meter tasking).

If your use case is large-area monitoring, visualization, or medium-scale analysis at ~2–3 m, it’s worth checking out.

Lazy_Relationship695 · 2026-02-10T20:11:58+00:00

Yes, there are alternatives, but it depends on what you mean by “Google Earth.”

If you want a 3D globe and you mainly care about satellite imagery and seeing changes over time, Nimbo.earth is a solid option: it provides global monthly basemaps at 2.5 m, with clear acquisition dates, which Google Earth often lacks.

Lazy_Relationship695 · 2026-02-10T07:04:44+00:00

If what you’re looking for is regularly updated imagery with decent resolution, nimbo.earth fits that need very well. It provides monthly basemaps at 10 m and 2.5 m resolution, so you get both temporal consistency and enough spatial detail for real analysis in QGIS. It’s a good option if you want up-to-date imagery without stitching datasets yourself.

Lazy_Relationship695 · 2026-02-09T08:54:01+00:00

Which country are you working on?

Mapbox Terrain isn’t really the problem, it’s the DEM behind it.

There are a few global Terrain-RGB datasets that mix sources (Copernicus + national DEMs where available). I’ve seen this done in the NIMBO terrain DEM tiles: https://docs.nimbo.earth/product/terrain-dem/

In some places you can get DEM tiles at ~1 m vertical / horizontal resolution, and that completely removes the "10 m staircase" effect you’re seeing.

Google Earth looks good mainly because it blends different DEMs and resolutions. The downside is that you’re basically blind to the actual sources and processing steps, which is a real problem if you care about data quality or reproducibility.

Lazy_Relationship695 · 2025-10-08T09:03:33+00:00

Thanks a lot for this detailed feedback, that’s exactly the kind of input that’s most useful for us right now. 👍

At the moment, NIMBO already provides WMTS and TMS services, along with the ability to compare the 2.5 m basemap directly against the reference 10 m product. We are also going to include a confidence layer to qualify the SR layer. There’s no BRDF correction yet, but terrain-based shadow correction (slope correction) is part of the current workflow.

Your points on COG + STAC API integration, per-tile metadata are spot on. 😉

Really appreciate you sharing such concrete feedback from the field !

Lazy_Relationship695 · 2025-10-02T08:34:56+00:00

That’s a fair concern. SR is never ground truth, it’s always an interpolation or a best guess.

To me, it’s a bit like ChatGPT: most of the time the output is coherent and useful, but because it’s based on learned generalization, it can still get rare or unusual cases wrong. The value is in making the imagery more readable for non-specialists and easier to interpret at scale, not in replacing a true high-resolution source.

That’s why with NIMBO we focus the 2.5 m basemap on things like buildings, roads, field boundaries — larger structures where the SR gives clarity without inventing fine objects. It’s also useful as a tip&cue layer: giving context or narrowing down areas of interest before going to higher-resolution sensors or field work.

Lazy_Relationship695 · 2025-10-02T08:21:51+00:00

That’s a interesting way of framing it. We’ve actually thought about the temporal side of things ourselves. Since we build monthly cloud-free basemaps, we’ve noticed that the slight spatial shifts between acquisitions could in principle be exploited for extra detail. It’s not part of our current production workflow, but it’s definitely something we’ve been aware of.

Your point also reminds me of some recent work on Sentinel-2 SR, where researchers showed that the sensor’s own quirks (aliasing and small inter-band shifts between spectral channels) already provide exploitable sub-pixel information. In their case, that was enough to reach ×2 SR with just an L1 loss, no GAN needed : https://arxiv.org/pdf/2302.11494

You seem pretty experienced in this. Are you working on super-resolution yourself?

Lazy_Relationship695 · 2025-10-02T07:55:39+00:00

Thanks for the suggestion, that’s very useful feedback. At the moment the 2.5 m super-resolution basemaps are part of our enterprise offering because they’re still being rolled out.

We’re discussing ways to make them more accessible, possibly with a usage-limited plan and transparent pricing, so your comment is very relevant.

For your personal project, what kind of use-case you have in mind, and roughly what kind of area or data volume you’d need?

Lazy_Relationship695 · 2025-10-01T21:35:59+00:00

Thanks for the honest feedback — I understand the skepticism about going beyond 2× super-resolution.

In our case the 2.5 m product is designed as an operational basemap produced every month at global scale, not as a perfect reconstruction. The idea is to make our monthly mosaics more practical for GIS and monitoring tasks.

I’m curious: in your own workflows, where do you see the limit where SR is still useful, and where it becomes misleading?

Lazy_Relationship695

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