The classic deep learning for computer vision example project starts out with a dataset containing images, and labels. However, when computer vision is needed for solving business problems, data is usually unlabeled and labeling the data is itself a challenge. This article walks through labeling images at scale and associated challenges — The classic example of a deep learning computer vision project starts out with a dataset containing images, and labels. Depending on what type of labels you have and the task you want to accomplish — image classification, object detection, or image segmentation; you would select from an appropriate set of…

OSMnx is a powerful Python package for road networks but does not incorporate current traffic conditions — let’s use the HERE traffic API to impute real-time traffic information in OSMnx — OSMnx is a powerful Python package for static road network information taken from OpenStreetMap- a free editable geographic database of the world. Here is a brief code snippet showing how OSMnx can be used to obtain the road network of Washington DC:

Tutorial on transfer learning using the Resnet-18 deep learning CNN to achieve a 97% validation accuracy — Deep Convolutional Neural Networks applied to image prediction tasks are extremely popular. It seems as though every few months there is a new algorithm that pushes the limits on CNN architectures. Just a few years ago these state of the art algorithms and the knowledge behind how to build them…

With Amazon announcing drone super-highways, it becomes all the more important to consider cyber-physical security in drones — What comes to mind when someone mentions drones? For me it’s one of two things: toy drones (like the one we recently gave our friend’s kid who turned 6) — or military drones and unfortunately, images like this:

SafeGraph provides dynamic location based data. Let’s use this to derive research insights. — On their website, SafeGraph mentions they have over 11M+ records for Points of Interest (POI) including data on location, brands, etc. Looking at the documentation, there are basically 3 kinds of data sets: places, geometry, and patterns (they recently introduced a new Spend data set as well). In this article…

Existing paradigms for integrating complex IT and OT architectures, insecurities, and building resilience — 2021 was full of cyber incidents with atypical consequences. The Colonial Pipeline ransomware attack caused gas shortages and panic buying for weeks. An intruder in a Florida water management system boosted levels of lye to 100 times higher than normal. JBS, the world’s largest meat supplier was attacked by ransomware…

A deep dive into recent TSA directives, critical infrastructure attacks, and the future of securing interdependent networks — On May 7th 2021, an employee at Colonial Pipeline saw a ransom note pop up in the control room, demanding cryptocurrency. Ultimately, Colonial Pipeline did pay the ransom of 4.4 million$ in bitcoin (a majority of which was recovered by the FBI). It was later found that the hackers did…

Fast, elegant geospatial visualizations of hundreds of thousands of traffic data points using Kepler.gl and HERE traffic data — Kepler.gl is Uber’s open source geospatial toolbox. Often, visualizing large-scale geospatial data sets can be quite challenging . There are many Python packages including Folium, GeoPandas, and Plotly that do a great job; but it can be a challenge to find the right plot backgrounds. …