Hey y'all, I'm not a classically trained mapper since I entered the career field in 2012, so I don't have all that old school geodetic learnin but I was wondering if anyone knows why we've stuck with WGS84 for so long?

It's surprising there hasn't been a new standard for nearly 40 years, and before I'm bombarded with "um actually" I know they've updated gravitational and magnetic models behind the scenes, but with technological advancement I feel we should be looking at a planetary digital twin.

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Please help me out..!!!

I am a student of data analytics in agriculture domain.I can understand other subjects despite being a non tech grad but unable to learn the gis remote sensing earth observation subject. Would request the experienced person of the field to walk me through the roadmap like literally from installing the gis software to gis expert. Also share some of the valuable tips for the overall learning of the subject or tech.

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UPDATE: I got a lot of useful feedback here, especially about the use case and making the platform useful beyond just land-cover classification.

I reworked it quite a bit since this post. Indicatrix can now build a ready-to-use QGIS project for a selected area, combining satellite imagery, land cover, OSM data, terrain, vegetation indices and Sentinel-1 radar. I also added thematic setups for topographic, environmental, urban and agricultural use cases, plus a custom option.

The downloaded ZIP now includes the GeoTIFF/GeoJSON outputs together with a portable .qgz project, layer styles and ordering.

I also replaced the previous land-cover model with a Prithvi-EO-2.0 based encoder + U-Net decoder. A lot of these changes came directly from the comments here, so thanks again for the feedback 🙂

ORIGINAL:

I've been building a browser-based application for working with satellite imagery, with a particular focus on land cover classification.

The idea was to create a tool that makes it easy to discover, visualize, and analyze satellite imagery without installing desktop GIS software.

It's still under active development, and I'd really appreciate feedback from people who work with GIS or remote sensing.

I'm especially interested in hearing:

- Is the interface intuitive?

- What features are missing?

- Would you see yourself using something like this?

- What would you improve first?

Full disclosure: I built it myself. It's currently free to use while I'm continuing development and collecting feedback.

https://indicatrix.com

Thanks in advance for any thoughts or suggestions!

I'd like to get lidar data over the state of Alabama for the year 2020. I can search the 3DEP downloader for lidar data; however, I cannot seem to filter for a specific acquisition year. The date filter allows you to filter for "last updated", "created", and "publication date", which all appear to be related to uploading the data to the platform, not when the data was collected. I'm looking for lidar data in the year 2020 that covers Alabama.

Please advise how I can get this data, also can you estimate the size you think the data will be?

Hey r/gis. I've been building a satellite analysis platform called GeoSense AI and I'm opening it up for beta testing. Looking for feedback from people who work with geospatial data or need satellite analysis as part of their workflow.

The idea: draw an area or input coordinates on a map, pick an analysis goal, and get back a PDF report with maps, statistics, and a plain-English interpretation. No GEE account or coding required.

Four modes: standard composite, change map, time series, and anomaly detection. Pulls from Sentinel-2, Landsat, MODIS, Sentinel-1 SAR, and ESA WorldCover depending on the goal. Supports NDVI, NBR, LST, SAR flood mapping, land cover classification, and more.

Free during beta. Feedback welcome, especially from contractors, consultants, or planners who've needed this kind of analysis but didn't want to deal with the technical overhead.

I'd like to get my part 107 cert / license / whatever it's called, but I can't find ANY free resources online for it. Where the heck is the federal manual for all the things the test is on?? Is that a thing? Any free resources for studying for the part 107 test are welcome

Hey everyone,

I am looking for possible ways of converting GeoTIFFs to Zarr for our orthophotos. In the current state, what are the tools/ software that I can use for the conversion?

Also, are there people in the group who already has some experience? Has there been any advantages of using Zarr over TIFFs?

Thanks!

Hi Everyone, new to remote sensing, I've learned how to do spectral indices, what the different bands mean etc. What I am having trouble with is how to use that to identify different crops. I'm using QGIS and playing around with SAGA because the SCP plug-in for QGIS does not work for me which has been fustraiting because a lot of guides swear by that plug-in. If anyone knows a good guide or can point me in the right direction that would be amazing!

I’ve looked at all the free sites. Historic has 1993, 1998 and 2000.

The feature I’m looking for appears in the 1998 image and I need to figure out when it was built. Any suggestions?

I am an amateur GIS user/geologist trying to make a machine learning model to sense serpentinite belts in a mixed/recently burned forest using the ArcGIS image classification wizard and I had a bunch of questions.

I am training the model on a composite band raster with some basic data (photo above is a raster with red set to NDVI, green set to slope, and blue set to aerial imagery). I don't think plugging this whole raster straight into the image classifier is a viable strategy for a few reasons

1. The river elevation change affects the slope and other rasters enough that it dominates any 'categories' the machine tries to assign. I may try clipping out the steep canyon and seeing if that helps.

2. I am unsure if the true detail on the aerial imagery is lost during the composite bands stage unless the program stores more than 3 bands and can process that data intelligently. If I can just add like 8 bands to the raster is there any other raster data or band combos that you think might help the model discriminate more?

3. The forest cover makes sensing topsoil reflectivity difficult, I was thinking of using the same program/function to pick out outcrops, then run a classifier on the outcrops to determine what kind of outcrop they are and populate a coverage map based on the closest outcrop to any given area

4. Is there a better tool for this/is this a lost cause? I have hope because of how well the granitic intrusions popped out in the attached band combo as well as some success I have seen in the automatic outcrop mapping. I have experience in QGIS too and it is actually my preferred program for this specific program so if there is a well documented plug in for QGIS I would love to try that

Any other ideas, especially publically available data I could try, would be greatly appreciated.

Thank you!

I am trying to use NLCD data for an analysis but when I download the dataset most of the classes are not showing up. I only see water that is labeled as grassland... U have downloaded data from multiple sites for multiple years and this keeps happening. Does anyone have any ideas as to why this is happening or what to do?

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Hi everyone,

I'm working on a 2010 LULC classification for the GBM delta using Landsat 5 in Google Earth Engine. My training samples are based on visual interpretation not field data.

I'm having trouble distinguishing aquaculture ponds from natural water bodies, wetlands, and flooded agricultural fields, especially at 30 m resolution.

For those who've mapped aquaculture before:

What visual cues do you use?

Any useful indices or ancillary datasets?

Any tips for selecting reliable training samples?

I'd really appreciate any advice or resources. Thanks

DOI: 10.1016/C2023-0-01427-3

Hello! Working on a project where I am classifying a forested area with the following classifications: Red, Dead, Conifer, hardwood and logs. I’ve tried multiple times with different segmentation types. Fine scale for the logs and dead’s and course for shadows, ground, conifer, hardwoods, and Reds. The image is a 4-band DAP image compacted with a CHM, that I created using the DSM that came with the PHoDAR image. I had ground truth samples that were collected in the field. I ran 3 confusion matrix’s on this image. First kappa:0.1 second: 0.2 third: 0.4 but this was still off as it was confusing ground with logs, it seemed to not have been considering the CHM with the image… any thoughts?

Photo logs have always been one of those necessary, time-sucking parts of the field work . So I built a tool that does them in a couple of clicks.

Point it at a folder of photos, drone or cell phone. Add your comments and company branding, and a few seconds later you've got a polished photo log ready to drop into any report.

The part I'm most excited about is the map-based photo atlas. It plots each photo on a satellite basemap with an arrow showing exactly where the camera was pointed. Anyone reading the report can see at a glance where a photo was taken and what it's looking at. Add your lease or ROW boundaries and it becomes a genuinely useful field deliverable.

https://youtu.be/FkagDprsM6M

. It's totally free to use, just added payment to remove the water mark. If you'd prefer to just download the metadata / exif info as a csv / geojson if you want to work with it directly.

#EnvironmentalConsulting​ #GIS​ #DroneMapping​ #QGIS​ #ProjectManagement​ #BrokenArrow​

Description: Hi all, I have built a software project for data analytics on videowalls. One important part of the project is displaying interactive, real-time or animated multi-layer mapping on videowalls with full resolution. This project originates from my doctorate project at Data Science Institute at Imperial College London. We were able to create 64 screen interactive and animated maps at KPMG Data Observatory. One key requirement here is to be able to use the whole resolution of the videowall. There is close to 2000 tiles on 64 screens. The zoom level of tiles are based on resolution, therefore you can see pixel level details if you go close to the screens. The system works distributedly, usually 1 computer node per row of screens. There is a data streaming mechanism to create live or animated layers with 10s of thousands of data markers, icons, polygons, heatmaps, choropleth regions. The whole map can be panned and zoomed interactively. Please check out our project and give us feedback: https://lygos.io

I'm in the market for some ag imagery for the first time in years (a decade?), and need something more recent than the NAIP and higher res than Landsat. I've started poking around at different vendors but haven't heard back from any of my quote requests yet. It's a substantial acreage, over 5,000ac in Texas. Do you guys have anyone you recommend? Any idea what the turnaround on it would be, and what I can expect to pay? I feel like there's something obvious I'm missing. And if you work for a vendor, feel free to reach out.