Fine-tuning a deep learning model for building footprints to be used for automatically counting new buildings in temporal panchromatic imagery

Change detection is an extremely important area of remote sensing. It helps us understand, given imagery or radar scans of given locations, what meaningful semantic changes occurred in categories of interest. Often those categories of interest include buildings /structures— these are the primary indicators of urban expansion and the size of population centers. However, classical change detection methods like statistical subtraction will often fail to accurately detect and isolate new structures. This is because optical data (such as panchromatic imagery)…


Visualizing Pytorch Models with Tensorboard’s Embedding Viewer

In many ways, deep learning has brought upon a new age of descriptive, predictive, and generative modeling to many dozens of industries and research areas. In light of its usefulness its also found a wealth of popularity, and with popularity often comes simplification. The most common simplification is that the power of deep learning is purely in the ability to predict. While deep networks have definitely reached new highs in accuracy of predicting complex phenomena, the situation is not nearly so simple. Before we can predict something, we must understand it. …


To many people’s dismay, there is still a giant wealth of paper documents floating out there in the world. Tucked into corner drawers, stashed in filing cabinets, overflowing from cubicle shelves — these are headaches to keep track of, keep updated, and just store. What if there existed a system where you could scan these documents, generate plain text files from their contents, and automatically categorize them into high level topics? Well, the technology to do all of this exists, and it’s simply a matter of stitching them all together and getting it to work as a cohesive system, which…


How much text and audio content have you consumed this week? Maybe you read a news article on “Flattening the Curve” and watched a Youtube video to understand what that really means? Maybe you finally looked up the lyrics to your favorite song and realized it was actually talking about something completely different than what you expected? Maybe you looked through some forum posts about your favorite hobby, read some Amazon reviews for a purchase you’ve been on the fence about, finally responded after reading that wall of text on the group message…

Well, how’d you do it?

Natural language…


With all the discussions about the dangers and ethics around emerging artificial intelligence technologies, we sometimes forget all the good that AI is doing in the world. For this article, I’ll outline some of the work we are doing at Esri using AI for improving disaster response.

We will ultimately be training and utilizing 3 separate deep learning models, using parallel processing to do inference against of aerial imagery, applying some advanced routing algorithms through the ArcGIS ecosystem, and finally utilizing web apps to share and keep track of response progress. This will result in a system that can consume…


Visual attention is so much a part of everyday life that most people never stop to think about why, or how, it happens. Focusing on the road while you’re driving, glancing at the food on your plate before you take a bite, looking at the text instead of the sidebars or screen bezel when you’re reading, attention is an obvious fact of life. Why look at the screen instead of around the whole room when someone asks you whether it’s Matt Damon or Mark Wahlberg in the movie?


In predicting real world events, there’s two main types of outcomes we can use to cover most questions. If the outcome we’re trying to predict can take on any and every value in some interval (such as temperature, bodyweight, speed, calories) then we say the outcome is continuous. If the quantity is really better represented as falling into a certain set of groups or buckets (such as cancer/not cancer, spam/not spam, car manufacturer, dog species) then we say the outcome is discrete.

Furthermore, when we are creating a statistical model to predict and draw inference about the outcome, we can…


In terms of unsupervised learning methods, some of the most well researched and common methods can be grouped under clustering. The basic idea is simple. If you can figure out how to define distances between data points, then data points that are closer together may exhibit some kind of group characteristic we could exploit for modeling or extract new understanding from. Some examples include patients with similar blood test results that have the same disease, consumers with a similar purchase history that are part of the same socioeconomic class or occupation, and flowers with similar colors and petal lengths that…

Shairoz Sohail

Data Scientist developing the cutting edge of geospatial machine learning with the GeoAI team @ Esri

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