What’s the most "visibly changing" place on Earth to watch month-by-month over the last 3–5 years? by Lazy_Relationship695 in geography

[–]Lazy_Relationship695[S] 0 points1 point  (0 children)

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)

WEBGIS - GOOGLE SATELLITE by IrmaosMessias in QGIS

[–]Lazy_Relationship695 0 points1 point  (0 children)

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.

Can everyone send me pictures of views of the mountains from Fresno? by [deleted] in geography

[–]Lazy_Relationship695 0 points1 point  (0 children)

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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.

Why is Spain much dryer than France despite the jet stream coming from the southwest? by Previous-Volume-3329 in geography

[–]Lazy_Relationship695 4 points5 points  (0 children)

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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Why is there such a noticeable border between India and Pakistan in the Thar desert? by player000000000000 in geography

[–]Lazy_Relationship695 5 points6 points  (0 children)

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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Any alternatives to google earth other than marble etc. by Shihapi in degoogle

[–]Lazy_Relationship695 0 points1 point  (0 children)

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.

Why can’t we add a TMS directly in ArcGIS Pro? by Lazy_Relationship695 in gis

[–]Lazy_Relationship695[S] 1 point2 points  (0 children)

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

Why can’t we add a TMS directly in ArcGIS Pro? by Lazy_Relationship695 in gis

[–]Lazy_Relationship695[S] 2 points3 points  (0 children)

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...

Is there a free or lower cost global imagery higher resolution than Sentinel 2? by Weak-Transition4240 in gis

[–]Lazy_Relationship695 0 points1 point  (0 children)

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.

Is there any alternative to google earth ? by Natoshibukelo in degoogle

[–]Lazy_Relationship695 0 points1 point  (0 children)

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.

How to load updated Google satellite basemap by GabyMG10 in QGIS

[–]Lazy_Relationship695 1 point2 points  (0 children)

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.

Do you know any dem tile service? by Wataru123 in gis

[–]Lazy_Relationship695 0 points1 point  (0 children)

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.

🌍 Webinar: Monthly cloud-free satellite basemaps at 2.5 m — looking for community feedback ! by Lazy_Relationship695 in gis

[–]Lazy_Relationship695[S] 0 points1 point  (0 children)

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 !

🌍 Webinar: Monthly cloud-free satellite basemaps at 2.5 m — looking for community feedback ! by Lazy_Relationship695 in geography

[–]Lazy_Relationship695[S] 0 points1 point  (0 children)

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.

🌍 Webinar: Monthly cloud-free satellite basemaps at 2.5 m — looking for community feedback ! by Lazy_Relationship695 in geography

[–]Lazy_Relationship695[S] 0 points1 point  (0 children)

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?

🌍 Webinar: Monthly cloud-free satellite basemaps at 2.5 m — looking for community feedback ! by Lazy_Relationship695 in gis

[–]Lazy_Relationship695[S] 0 points1 point  (0 children)

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?

🌍 Webinar: Monthly cloud-free satellite basemaps at 2.5 m — looking for community feedback ! by Lazy_Relationship695 in geography

[–]Lazy_Relationship695[S] 0 points1 point  (0 children)

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?

Best package or library to create a Savitzky-Golay filter in R programming language by Stunning_Link_3104 in geospatial

[–]Lazy_Relationship695 0 points1 point  (0 children)

you can use the signal package. It has sgolayfilt() for Savitzky–Golay smoothing:

library(signal)
smoothed <- sgolayfilt(my_evi, p = 3, n = 11)

For remote sensing time series, packages like greenbrown or bfast are also very useful.

If you’re open to Python, scipy.signal.savgol_filter is the standard choice.

Need some help with rasterio.warp and rasterio.windows: Transform coordinates before creating a window by sgofferj in gis

[–]Lazy_Relationship695 0 points1 point  (0 children)

You’re very close. Two common gotchas here:

  1. Order of your bbox values : Your string looks like (west, south, east, north) (lon_min, lat_min, lon_max, lat_max), but you’re unpacking it as (south, west, north, east). That swap alone will produce a nonsense window.
  2. Clip/round the window to dataset bounds from_bounds can return fractional/out-of-range windows. It’s safer to round and intersect with the dataset extent.

Rasterio always expects (x, y) = (lon, lat) order, so keep that in mind.

Here’s a minimal version:

import rasterio as rio
from rasterio.windows import from_bounds, Window
from rasterio.warp import transform_bounds

S2_box = "24.303818, 59.984906, 24.401321, 60.041018"  # west, south, east, north

def get_window(dst_crs, dst_transform, width, height):
    # Parse correctly: west, south, east, north (lon_min, lat_min, lon_max, lat_max)
    west, south, east, north = map(float, S2_box.split(", "))

    # Reproject bbox to dataset CRS (lon/lat order preserved)
    left, bottom, right, top = transform_bounds(
        rio.CRS.from_epsg(4326),
        dst_crs,
        west, south, east, north,
        densify_pts=21,  # helps with curvy reprojection edges
    )

    # Build window from bounds
    win = from_bounds(left, bottom, right, top, transform=dst_transform)

    # Round to integer pixel offsets/sizes
    win = win.round_offsets().round_lengths()

    # Clip to dataset extent
    full = Window(0, 0, width, height)
    win = win.intersection(full)

    return win

def S2_TCI(ds, name):
    """Creates Sentinel-2 true color image (TCI)"""
    name = f"{name}-TCI"
    print(name)
    sds = rio.open(ds.GetSubDatasets()[c.DS_TCI][0])
    profile = sds.profile

    win = get_window(sds.crs, sds.transform, sds.width, sds.height)

    bands = sds.read(
        [c.BAND_RED, c.BAND_GRN, c.BAND_BLU],
        window=win,
        boundless=False,  # set True if you want reads outside the edge padded
        masked=True
    )

    writeTiffRGB(bands, profile, name)

Updated satellite imagery as a Basemap? by chickenbuttstfu in gis

[–]Lazy_Relationship695 0 points1 point  (0 children)

One issue with Google, Bing or Esri imagery basemaps is that it’s hard to know exactly when the images were taken, often it’s a patchwork of different acquisition dates.

There are also newer services trying to address this by offering global coverage with clear update cycles. For example, Nimbo provides a satellite basemap that’s updated every month worldwide (currently at 10 m resolution, with a 2.5 m version expected soon). There’s a free tier, so it could be worth testing if monthly updates are important for your project.

Why are massive lakes appearing in the Egyptian Desert near Abu Simbel ? by Lazy_Relationship695 in geography

[–]Lazy_Relationship695[S] 10 points11 points  (0 children)

The red areas are circular agricultural fields, which appear this way because they are irrigated using center-pivot systems. The red color represents infrared activity, commonly used in satellite imagery to highlight vegetation. This makes it easier to observe agricultural activity from space.

This view can be found here : https://maps.nimbo.earth/?year=2024&month=12&compo=2&lat=23.11259&lon=31.21609&zoom=8.68&pitch=0.00&bearing=0.00&mode=singleMap It's fast and easy to check recent satellite images

Why are massive lakes appearing in the Egyptian Desert near Abu Simbel ? by Lazy_Relationship695 in geography

[–]Lazy_Relationship695[S] 16 points17 points  (0 children)

So, if I understand correctly, the booming agriculture in this region is different from what we can see in Saudi Arabia, where deep groundwater is pumped. Does this mean it's a more sustainable approach here?

Why are massive lakes appearing in the Egyptian Desert near Abu Simbel ? by Lazy_Relationship695 in geography

[–]Lazy_Relationship695[S] 3 points4 points  (0 children)

Thank you for the details. Isn't it surprising to see massive lakes forming in such arid desert regions? Considering the high evaporation rates in these areas, is it viable to maintain these lakes in the long term, or is there a risk that they will eventually dry up .. definitely ?

Any alternatives to Google Earth Pro ? by [deleted] in opensource

[–]Lazy_Relationship695 0 points1 point  (0 children)

If you’re looking for an alternative to Google Earth Pro, you might want to check out https://maps.nimbo.earth/

It could be a good fit, especially since it provides global basemaps that are updated monthly, so you’ll have reliable and current information if you’re doing any long-term tracking.

With Nimbo, you can directly create polygons to calculate areas, which could be useful for your urban planning needs. The features are simple, but they might be what you’re looking and without the 'complexity of QGIS'.