I made an AI assistant add-in that runs inside ArcGIS Pro. I teach GIS at the university level and do consulting work in ArcGIS Pro. Like everyone, I've watched students and colleagues keep ChatGPT open in a second window while working in Pro. The AI describes what buttons to click, you have to do the work, and half the answers are wrong because the AI can't see the project.

So I built Waldo, an add-in that puts the assistant inside Pro, connected to your live session. It can see your maps, layers, and schemas. It can advise, but it can also do the work: runs geoprocessing tools, sets symbology and labels, builds and exports layouts. The video is an unscripted session from my own work: mapping uninsured rates by county with graduated colors, then overlaying state Medicaid-expansion status once a pattern jumped out, and finishing with a print-ready layout of the lower 48. About ten minutes of real time, cut down here.

Design choices:

• Bring your own model. It works with an Anthropic or OpenAI API key, OpenRouter, or fully local models via Ollama/LM Studio. No subscription to me, and your data never touches my servers. The add-in itself never touches the network. The only outbound traffic is your own API calls to whichever provider you configured.

• There's an enforced read-only "Ask" mode — the assistant can inspect and explain your project but can't change anything. Enforced at the tool level, not a polite prompt.

• Built for people who don't trust AI output (I'm one of them): every geoprocessing run shows the exact parameters it used, and one click reopens the tool dialog pre-filled with them, so the AI's work is always reproducible by hand.

I use it in my own consulting work (it recently turned a multi-hour needs-assessment mapping job into an afternoon), and it's built around my 30 years of cartographic habits and best practices. It defaults to sane symbology, legends, and layouts instead of whatever an LLM improvises.

7-day free trial, $5 one-time after that, no accounts needed: waldogis.com

Genuinely interested in community reaction. Happy to answer anything about how it works and what it can and can't do.

Uber has transformed the way we think about going from X to Y. Imagine dominating one of the biggest sectors of the US economy, without any significant working capital or inventory in just 5.5 years!
Uber’s success can definitely be attributed to some of those amazing features such as tracking, location pick up and drop, fare estimation and so on. But, one of the prime challenges that the company used to dread in the early days was the unavailability of the drivers when customers demanded for a cab!
The company solved this problem in one of the most innovative ways: through "monetization".

You must have heard about Uber’s most famous surge pricing. What is surge pricing? Why surge pricing? How is it calculated? How does it help in matching supply and demand?

Get answers to these questions in the next piece: https://medium.com/locale-ai/how-does-uber-do-price-surge-using-location-data-cfee03415022

Portable Tile Server Android can serve data to the same EUD End User Device or any device on the same network and includes 2D and 3D Maps in browser. Serves geospatial data OGC API Features, OGC API Tiles/XYZ/TMS/WMTS and dynamic and cached vector tiles to raster tiles, serving 3DTILES, quantized mesh terrain tiles, distribute source data too https://techmaven.net/portabletileserver

Hi all,

I'm going to be an intern at an institute specializing in GIS and I needed help regarding project ideas.

I have googled a lot and compiled a few topics, but I was hoping you guys would help out with some ideas well.

Thanks!

https://youtu.be/0QLgHCeX3RE

What app do you guys like more and you think provides the best geospatial info? I’ve seen the same question on different Reddit communities but I’m curious about this one!:) I’m a big Google Maps fan personally because I’m able to easily save locations and organize them into lists, but that’s the only reason. Besides that, I love the Apple Maps overall layout and the ability to see when the sun’s setting for other countries on the satellite layer. The app also runs a lot smoother, but that’s probably only because I have around 2,000 locations saved on my google maps lol. Oh and the directions, I like apple maps directions more :)

Hi, I'm an outsider to the geospatial industry but for some time I've been looking into how stuff takes place in here. One thing I found out was that for any geospatial company GIS is a big component of the manpower that goes in. I wanted to understand how intensive the GIS work is, how automate-able the work is and how big of a market opportunity would one have here.

PS: I'm also looking for enthusiastic folks to come and possibly build this with me! I have a solid background in ML

Hey everyone,

My team and I are working on a new approach to handling large-scale geospatial imagery, and I'd be incredibly grateful for some real-world feedback from the experts here.

My background is in ML, and we've been tackling the problem of data infrastructure. We've noticed that as satellite/drone imagery archives grow into the petabytes, simple tasks like curating a new dataset or finding specific examples can become a huge bottleneck. It feels like we spend more time wrangling data than doing the actual analysis.

Our idea is to create a new file format (we're calling it a .cassette) that stores the image not as raw pixels, but as a compressed, multi-layered "understanding" of its content (e.g., separating the visual appearance from the geometric/semantic information).

The goal is to make archives instantly queryable with simple text ("find all areas where land use changed from forest to cleared land between Q1 and Q3") and to speed up the process of training models for tasks like land cover classification or object detection.

My questions for you all are:

1. Is this a real problem in your day-to-day work? Or have existing solutions like COGs and STAC already solved this for you?

2. What's the most painful part of your data prep workflow right now?

3. Would the ability to query your entire archive with natural language be genuinely useful, or is it a "nice-to-have"?

I'm trying to make sure we're building something that actually helps, not just a cool science project. Any and all feedback (especially the critical kind!) would be amazing. Thanks so much for your time.

My degree is in Earth Sciences with a concentration in GIS. I did some work associated with GIS for $ 50 - $ 80 an hour for five of the last ten years. Working primarily in the field of health (cluster analysis and analysis of fraud). I now work exclusively in systems analysis for $ 80/hr, but my real interest is GIS. I can not justify taking a step down or sideways ($$$). I wonder if I should keep hoping to earn a spot in GIS @>$ 80 an hour? Can r/Geospatial give me hope and ideas?

"phenomena with a spatial reference" is the same as "spatial phenomena"?

or is wrong to use the terminology " phenomena with a spatial reference"

During monsoon season, it's a difficult task to get 100% cloud-free images, but we have a solution for this. We have Synthetic Aperture Radar (SAR) images which provide 100% cloud-free images even during monsoon. EOfactory makes it easier to have the Sentinel 1 SAR image download option, where you can find images for any part of the world, and download them easily at a faster rate.

Recently, we used SAR images for crop classification for the Belgaum study area of 13410.81 sq. km, India through the EOfactory platform.

The workflow adopted is as follows-

1. Sentinel 1 SAR data & Sentinel 2 data were acquired for June to November 2020.

Here, the main work is on SAR images, Optical images are for reference.

2. SAR images stacked from June to November 2020 with VH only.(Most preferable for crop analytics).

3. Based on crop calendar, and leaflet maps like Google Earth, signatures of the training samples were taken for different classes.

4. Using EOfactory/Pixel Magic, the classification result image is generated.

Below, there are images showing a single SAR stacked image can replace a set of optical images during monsoon season for better results; SAR classified map with legend at different scales, classified image with acreage.

Join EOfactory (https://eofactory.ai/) for FREE Now to do your own analysis.