USGIF's GEOINT Community Week brings together the defense, intelligence, homeland security, and geospatial communities at-large for a week of briefings, educational sessions, workshops, technology exhibits and networking opportunities. USGIF is looking for volunteers to share our Intro to GEOINT presentation at your local schools during GEOINT Community Week. This is a great way to give back by helping EdGEOcate our future leaders. We have prepared presentation materials for you that are geared toward upper elementary through lower high school grades and provide an overview of GEOINT--geography, maps, satellites, imagery, remote sensing, GIS, and careers. The presentation takes 45 minutes to one hour and is highly interactive with games, Q&A, stories, videos, and much more.

Flooding is a destructive and dangerous hazard and climate change appears to be increasing the frequency of catastrophic flooding events around the world. Physics-based flood models are costly to calibrate and are rarely generalizable across different river basins, as model outputs are sensitive to site-specific parameters and human-regulated infrastructure. In contrast, statistical models implicitly account for such factors through the data on which they are trained. Such models trained primarily from remotely-sensed Earth observation data could reduce the need for extensive in-situ measurements. In this work, we develop generalizable, multi-basin models of river flooding susceptibility using geographically-distributed data from the USGS stream gauge network. Machine learning models are trained in a supervised framework to predict two measures of flood susceptibility from a mix of river basin attributes, impervious surface cover information derived from satellite imagery, and historical records of rainfall and stream height. We report prediction performance of multiple models using precision-recall curves, and compare with performance of naive baselines. This work on multi-basin flood prediction represents a step in the direction of making flood prediction accessible to all at-risk communities.

Maharashtra has put into action Artificial Intelligence to alleviate agricultural hazards by making use of analysed data to fill in any clefts. The project so named, is the Maha Agri Tech project and had become operational in January this year. The Artificial Intelligence (AI) being employed in the first phase are the satellite images, based on mining data together from by the Maharashtra Remote Sensing Application Centre (MRSAC) and the National Remote Sensing Centre (NRSC) in Hyderabad. Moving on to its second phase, (in the upcoming rabid season) a yield model would be constructed wherein, data sets from different data providers will be amalgamated to create a territorial database of soil nutrients, rainfall, moisture stress and a few other relevant factors. This as a consequence will promote location specific consultation to farmers.

In all types of disasters, from earthquakes to armed conflicts, aid workers need accurate and timely data such as damage to buildings and population displacement to mount an effective response. Remote sensing provides this data at an unprecedented scale, but extracting operationalizable information from satellite images is slow and labor-intensive. In this work, we use machine learning to automate the detection of building damage in satellite imagery. We compare the performance of four different convolutional neural network models in detecting damaged buildings in the 2010 Haiti earthquake. We also quantify how well the models will generalize to future disasters by training and testing models on different disaster events.

Knowledge about frequency and location of snow avalanche activity is essential for forecasting and mapping of snow avalanche hazard. Traditional field monitoring of avalanche activity has limitations, especially when surveying large and remote areas. In recent years, avalanche detection in Sentinel-1 radar satellite imagery has been developed to overcome this monitoring problem. Current state-of-the-art detection algorithms, based on radar signal processing techniques, have highly varying accuracy that is on average much lower than the accuracy of visual detections from human experts. To reduce this gap, we propose a deep learning architecture for detecting avalanches in Sentinel-1 radar images. We trained a neural network on 6345 manually labelled avalanches from 117 Sentinel-1 images, each one consisting of six channels with backscatter and topographical information. Then, we tested the best network configuration on one additional SAR image. Comparing to the manual labelling (the gold standard), we achieved an F1 score above 66%, while the state-of-the-art detection algorithm produced an F1 score of 38%. A visual interpretation of the network's results shows that it only fails to detect small avalanches, while it manages to detect some that were not labelled by the human expert.

Launched in January this year, the Maha Agri Tech project seeks to use technology to address various cultivation risks ranging from poor rains to pest attacks, accurately predict crop-wise and area-wise yield and eventually to use this data to inform policy decisions including pricing, warehousing and crop insurance. When farmers in six districts of Maharashtra begin sowing for the coming rabi season, this project will enter its second phase where artificial intelligence and satellite imagery will be used to mitigate risks. Fields of the farmers that are part of the project will be monitored via satellite images at every stage right until the harvest. In its first phase the Maha Agri Tech project used satellite images and analysis from the Maharashtra Remote Sensing Application Centre (MRSAC) and the National Remote Sensing Centre (NRSC) in Hyderabad to assess the acreage and the conditions of select crops in select talukas. In its second phase, various data sets from diverse data providers will be combined to build yield modelling and a geospatial database of soil nutrients, rainfall, moisture stress and other parameters to facilitate location-specific advisories to farmers.

If your home is in a wildfire area, insurance companies tend to not want to go anywhere near it. But "wildfire areas" tend to be pretty broad. What if companies could evaluate the risk on a more granular level -- tapping things like satellite imagery and machine learning combined with wind, weather and topology data, to better define the riskiest zones? Could more home owners be offered policies, and at more affordable rates? Delos itself doesn't act as the insurer; instead, it acts as a Managing General Agent (or MGA) for a bunch of major carriers.

Creating accurate maps today is a painstaking, time-consuming manual process, even with access to satellite imagery and mapping software. Many regions -- particularly in the developing world -- remain largely unmapped. To help close this gap, Facebook AI researchers and engineers have developed a new method that uses deep learning and weakly supervised training to predict road networks from commercially available high-resolution satellite imagery. The resulting model sets a new bar for the state of the art for accuracy, and because it is able to accommodate regional differences in road networks, it can effectively predict roads around the globe. We are now sharing the details of our model and making data available to the global mapping community through Map With AI, a new set of specialized map-editing services and tools. Map With AI includes an editor interface, RapiD, which allows mapping experts to easily review, verify, and adjust the map as needed.