Unsupervised flood mapping

Floods can have a catastrophic impact on local communities and their economies, resulting in loss of human life and property, damage in agriculture and destruction of infrastructure. Accurate and near real-time derived flood maps are of paramount importance in directing search and rescue operations and relief efforts. We have developed a practical and scalable solution to flood mapping in the form of an unsupervised flood water detector which utilizes 4- or 8-band DigitalGlobe imagery and can process an entire strip in a matter of minutes on a default GBDX instance.

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Building detection on mosaics using deep learning

Picture being able to select an arbitrary region of the world in your browser and within it rapidly locate all the objects of interest. The advancement of deep learning combined with easy access to high resolution satellite imagery enabled by platforms such as GBDX are making accurate object detection at scale an attainable goal. However, the application of deep learning on satellite imagery is still in its infancy and many questions are open with regards to its efficacy at a global scale.

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Assessing OSM with automatic built-up detection

OpenStreetMap (OSM) is a free global map built from scratch by volunteers around the world. It is a living map of the world: online communities of mappers use satellite imagery and/or knowledge of the area they live in to continuously update the map with buildings, roads, infrastructure, etc. The OSM dataset has diverse uses, from locating remote population centers in order to administer aid, to training deep neural networks to identify roads on satellite imagery.

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Oil tanks

Estimating oil reserves from high-resolution satellite imagery has become rather fashionable in our budding geospatial-analytics-from-space industry. Oil is typically stored in tanks with floating roofs, therefore the fill of the oil tank can be estimated from the shadow cast on the inside of the tank as the lid sinks. A pretty neat idea.

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Detecting population centers in Nigeria

There are large regions of the planet which, although inhabited, remain unmapped to this day. In the past, DigitalGlobe has launched crowdsourcing campaigns to detect remote population centers in Ethiopia, Sudan and Swaziland in support of NGO vaccination and aid distribution initiatives. Beyond DigitalGlobe, there are other initiatives under way to fill in the gaps in the global map, aiding first responders in their effort to provide relief to vulnerable, yet inaccessible, people.

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Creating a GBDX task

Tasks are the bread and butter of the GBDX Platform. This walkthrough will take you through the steps of creating a task that can run your Python code on GBDX. We will start with a very simple task and then cover the more advanced case of creating a machine learning task. We will also demonstrate how you can setup your machine learning task to run on a GPU.

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Finding swimming pools in Australia

Information about built environments is extremely valuable to insurance companies, tax assessors, and public agencies, as it empowers a wide range of decision-making including urban and regional planning and management, risk estimation, and emergency response. Extracting this information using human analysts to scour satellite imagery is prohibitively expensive and time consuming. Feature extraction and machine learning algorithms are the only viable way to perform this type of attribution at scale.

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