Between the Poles

New satellite constellation will capture 30 cm imagery with up to 15 revisits per day

I remember back in August 2014, when the first images from WorldView-3 were being downloaded. WorldView-3 was the first one-foot-resolution commercial satellite. It operated at an altitude of 617 km, flew in a sun synchronous orbit and could collect up to 680,000 km² per day.  It had 31 cm panchromatic resolution, could capture 680,000 km² per day, and had an average revisit time of less than a day.  In 2021 MAXAR is planning to launch the WorldView Legion constellation, six satellites with 30 cm resolution, better than 5 meter absolute accuracy, able to capture 5 million km² every day, and with up to 15 revisits per day.

At GeoIgnite Online 2020 Kumar Navulur, Chris Formeller, and Kathryn Seitz presented an overview of the specifications and capabilities of the Legion constellation. The total land mass of the Earth is 148.9 million km2.  The new constellation will be able to capture 30 cm imagery of over 3 % of that every day.  Over much of the lower 48 states in the U.S. it will be able to revisit the same location 15 times per day. Over Canada it will be closer to 8 revisits per day.  Some of the satellites will fly sun synchronous orbits and others mid-inclination orbits.  What this means practically is that it will be possible to capture imagery at different local times of the day, unlike with sun synchronous orbits where imagery is captured at the same local time every day. For many customers imagery will be available within 24 hours of capture.

New Radarsat Constellation imagery will be available to public at no cost

Canada's first synthetic aperture radar satellite was launched in 1995. Since then Radarsat-2 was launched in 2007 and continues to operate.  In June of this year the Radarsat Constellation (RCM) was launched. At the GeoIgnite conference Sergey Samsonov, Canada Centre Remote Sensing (NRCan), explained that in addition to the technical improvements such as a revisit time of four days and data latency for some applications as low as 10 minutes, RCM data will be made available at no cost.  This is a different business model from Radarsat-1 and Radarsat-2 and is intended to reduce entry costs for developers and entrepreneurs.  The RCM data will be available through the Earth Observation Management System.  In addition earlier this year the Canadian Space Agency and the Canada Centre for Mapping and Earth Observation made RADARSAT-1 images of Earth available to researchers, industry and the public at no cost. The 36,500 images are available through the Earth Observation Data Management System.

One of the primary benefits of radar is that it is not affected by cloud cover.  Radarsat-2 and RCM are capable of one meter resolution, although five meter is more typical.  In addition the Radarsat satellites are capable of using interferometry to measure centimeter movements and deformations of the Earth's surface in the horizontal and vertical directions.  In his talk Sergey Samsonov showed some examples from Radarsat-2 of subsidence in Vancouver, Seattle, Mexico City, Alberta, and other locations detected by radar interferometry.  I have blogged about an application Network Alert developed by Planetek that uses satellite radar interferometry to detect ground subsidence and to alert municipal water network operators of possible water leaks.

The new constellation dramatically improves the ability to monitor changes on the Earth's surface. The revisit time for the new constellation is much shorter. While the previous satellites had a revisit time of 24 days, the new RCM satellites will revisit the same location every four days with a greater frequency in Canada's fat north. The time between data acquisition and the data being available with the RCM constellation is remarkable. The data latency for the new RCM constellation depends on the type of data. For ships in Canadian waters it is 10 minutes. For other maritime surveillance it is 30 minutes. For Canadian and global disaster management it is 2 hours - and remember it is not affected by cloud cover.  For ecosystem monitoring applications the daya will be available in 24 hours. Pre-defined recurrent observation scenarios (standard coverages) will be made available to users in advance of acquisition.

The RCM is designed for three main areas: maritime surveillance (ice, surface wind, oil pollution and ship monitoring), disaster management and environmental monitoring. While the mission design initially focused on maritime security requirements, land security, particularly in the Arctic, will be dramatically enhanced. In addition economic growth is also being targetted.  Developers and entrepreneurs are being encouraged by an open data policy to develop a wide range of applications in Canada and internationally.

Applying machine learning to identifying housing starts from satellite imagery

Deep learning is increasingly finding practical applications using geospatial data, specifically, satellite imagery. At the GeoIgnite conference in Ottawa, Nicolas Martinez of Statistics Canada described a project scheduled to kick-off in July to use machine learning to identify housing starts across Canada.  The goal of the project is to improve the accuracy and coverage of housing starts survey, specifically to fill data gaps for smaller remote and indiginous communities which are generally excluded from the current survey program. 

Canada Mortgage and Housing Corporation (CMHC) currently employs a significant number of people at their head office in addition to field agents across the country who physically confirm residential starts and completions.  The objective of the project is to apply convolutional neural network (CNN) machine learning to satellite imagery to extract information about housing starts and completions. The imagery will be preprocessed to prepare it to be used in developing a machine learning processing model.  The largest challenge will be developing training and testing imagery data sets.

The original application of deep learning to photo imagery was by Geoffrey Hinton at Google who used it to distinguish dogs and other objects in photos that people uploaded.  More recent applications of satellite imagery targeted landuse and agricultural identification. For example, remote sensed imagery from a satellite can be used to create a model that will differentiate between corn and potatoes, using factors that can be calculated from satellite imagery such as normalized difference vegetation index (NDVI) (min, max, mean), texture (min, max, mean), vegetation height, and geometric factors such as orientation.  I blogged about how open source code and publicly available training data has been applied to track deforestation and reforestation in Mato Grosso, a state in the central amazon region of Brazil. A study in the Netherlands used CNN to identify blocked waterways using overflight imagery with a success rate of 97 %.

An important application of satellite imagery is identifying built structures. I have blogged about how satellite imagery has been used to identify buildings and transportation networks by applying a neural network model together with OpenStreetMap layers and high resolution Worldview multispectral imagery.  NVIDIA has demonstrated the ability to automate detection of many road networks using deep learning algorithms and multi-spectral high resolution imagery.  Another application of CNN and imagery in the Netherlands was able to differentiate residential roofs with dormer windows from those without.

The essential key to effective application of deep learning is good training and test data - ground-truthed data that involves, for example, someone on the ground identifying whether the fields seen in the imagery are corn or potatoes or something else. There are  publicly available training data sets that can be used to train satellite imagery CNN models. For example, I blogged about the System for Terrrestrial Ecosystem Parameterization (STEP) dataset which has 2000 manually labeled sites covering 17 different land cover types scattered across all continents. A large publicly available training dataset derived from Sentinel 2 imagery contains 30,000 polygons of land use training data for ten classes of land use: annual crop, forest, herbaceous vegetation, highway, industrial, pastures, permanent crop, residential, river and sea or lake.

The Statistics Canada project is scheduled to begin in July with initial work on a proof of concept and CNN testing to begin in August and September.  The communities chosen for the initial phase are  Kitchener-Cambridge-Waterloo, Red Deer, and Iqaliut.  The first phase is scheduled to be completed by winter 2019.  The project plans to use Tensorflow and Keras, open source libraries and a python framework for applying machine learning.  Artificial intelligence (AI) has often suffered from inflated expectations, complex code, heavy processing requirements and not very practical applications. There is a growing body of practical applications in the geospatial domain that shows that as a result of the immense processing power available in the cloud machine learning is able to generate fairly easily practical and useful results.  As Chris Holmes pointed out in his talk at FOSS4GNA, the challenge for Statistics Canda will be to develop reliable and publicly available training and test datasets to enable deep learning models to be created for a broader range of applications using the huge volumes of satellite imagery and other geospatial data that are now available.

Deep learning is increasingly finding practical applications using geospatial data, specifically, satellite imagery. At the GeoIgnite conference in Ottawa, Nicolas Martinez of Statistics Canada described a project scheduled to kick-off in July to use machine learning to identify housing starts across Canada.  The goal of the project is to improve the accuracy and coverage of housing starts survey, specifically to fill data gaps for smaller remote and indiginous communities which are generally excluded from the current survey program. 

Canada Mortgage and Housing Corporation (CMHC) currently employs a significant number of people at their head office in addition to field agents across the country who physically confirm residential starts and completions.  The objective of the project is to apply convolutional neural network (CNN) machine learning to satellite imagery to extract information about housing starts and completions. The imagery will be preprocessed to prepare it to be used in developing a machine learning processing model.  The largest challenge will be developing training and testing imagery data sets.

The original application of deep learning to photo imagery was by Geoffrey Hinton at Google who used it to distinguish dogs and other objects in photos that people uploaded.  More recent applications of satellite imagery targeted landuse and agricultural identification. For example, remote sensed imagery from a satellite can be used to create a model that will differentiate between corn and potatoes, using factors that can be calculated from satellite imagery such as normalized difference vegetation index (NDVI) (min, max, mean), texture (min, max, mean), vegetation height, and geometric factors such as orientation.  I blogged about how open source code and publicly available training data has been applied to track deforestation and reforestation in Mato Grosso, a state in the central amazon region of Brazil. A study in the Netherlands used CNN to identify blocked waterways using overflight imagery with a success rate of 97 %.

An important application of satellite imagery is identifying built structures. I have blogged about how satellite imagery has been used to identify buildings and transportation networks by applying a neural network model together with OpenStreetMap layers and high resolution Worldview multispectral imagery.  NVIDIA has demonstrated the ability to automate detection of many road networks using deep learning algorithms and multi-spectral high resolution imagery.  Another application of CNN and imagery in the Netherlands was able to differentiate residential roofs with dormer windows from those without.

The essential key to effective application of deep learning is good training and test data - ground-truthed data that involves, for example, someone on the ground identifying whether the fields seen in the imagery are corn or potatoes or something else. There are  publicly available training data sets that can be used to train satellite imagery CNN models. For example, I blogged about the System for Terrrestrial Ecosystem Parameterization (STEP) dataset which has 2000 manually labeled sites covering 17 different land cover types scattered across all continents. A large publicly available training dataset derived from Sentinel 2 imagery contains 30,000 polygons of land use training data for ten classes of land use: annual crop, forest, herbaceous vegetation, highway, industrial, pastures, permanent crop, residential, river and sea or lake.

The Statistics Canada project is scheduled to begin in July with initial work on a proof of concept and CNN testing to begin in August and September.  The communities chosen for the initial phase are  Kitchener-Cambridge-Waterloo, Red Deer, and Iqaliut.  The first phase is scheduled to be completed by winter 2019.  The project plans to use Tensorflow and Keras, open source libraries and a python framework for applying machine learning.  Artificial intelligence (AI) has often suffered from inflated expectations, complex code, heavy processing requirements and not very practical applications. There is a growing body of practical applications in the geospatial domain that shows that as a result of the immense processing power available in the cloud machine learning is able to generate fairly easily practical and useful results.  As Chris Holmes pointed out in his talk at FOSS4GNA, the challenge for Statistics Canda will be to develop reliable and publicly available training and test datasets to enable deep learning models to be created for a broader range of applications using the huge volumes of satellite imagery and other geospatial data that are now available.

A killer application for satellite imagery collected daily for the entire earth

At the GeoIgnite conference in Ottawa, Wade Larsen of Urthecast described what may be a killer application for satellite imagery captured daily. Farmers in the US over-irrigate by 25% per year. A recent study has demonstrated that monitoring vegetation growth on a daily basis can save farmers $50 000 in water per year. With 260,000 pivots operating in the US, this amounts to billions of dollars of savings every year.

Urthecast has been known for having two cameras (Iris and Theia) on the International Space Station and two Deimos earth observation satellites. Last year Urthecast announced the acquisition of Geosys Technology, a software company that processes earth observation imagery for agricultural applications.  Their latest initiative is called UrtheDaily with the objective of capturing nine bands of imagery from six satellites covering the Earth's entire landmass every day. The nine bands are identical to Sentinel-2's visual and near-infrared bands. The cameras are cross-calibrated with Sentinel-2 to assure data compatibility. The six cameras enable a daily revisit for higher latitudes and somewhat less for tropical areas. It captures imagery at the same time, from the same altitude every day.  About 150 terabytes will be acquired and downloaded every day. The imagery has five meter resolution (GSD) and represents scientific grade data quality that will be available on the cloud for user access within hours of acquisition. UrtheDaily is designed to support machine-learning and AI ready geo-analytics applications on a global scale. The UrtheDaily platform will provide the ability to discover data, run user algorithms, and deliver data to customers.

UrtheCast reports that it has already signed contracts primarily in the agriculture sector. An example of a potential killer app in agriculture is monitoring irrigation. Farmers in the U.S. overwater by 25 %. A four year in Texas by HydroBio which assessed the value of frequent monitoring of plant health and growth using satellite data concluded that to be effective this required high-frequency (daily) monitoring with highly calibrated sensors. With its resolution UrtheDaily will be able to monitor vegetation for individual pivots on a daily basis in order to optimize water utilization. It is estimated that this could save farmers $50,000 per pivot per year. (A pivot typically irrigates 120 acres.) There are 260,000 pivot irrigation systems in the U.S. which translates into the potential to save farmers billions of dollars per year.

To date UrtheCast reports that it has signed $350 million worth of contracts in the U.S., Russia, India and other countries. It plans to launch six satellites in 2021 and become operational in 2022.

Measuring emissions from natural gas superemitters from space

Four countries have launched satellites which can measure concentrations of greenhouse gas emissions. Canada's GHGSat Inc is the first private company that can measure emissions from any location on earth. To date GHGSat's Clair satellite has measured emissions from 2,500 locations.  Among these are emissions from so-called superemitters in the natural gas industry in the U.S.

I have blogged extensively about methane emissions from the shale gas industry. There is increasing evidence that leakages from production wells, of which there are about half a million in the U.S., erodes the advantage that natural gas has over coal with respect to contributing to greenhouse gas warming. Studies suggest that a small number of wells, socalled superemitters, are responsible for more than half of the total volume of leaked methane gas in the United States.  This is an image of a heat map created from measurements made by GHGSat's Claire August 2018 over a site in Texas.  The heat map has been superimposed on imagery captured by ESA's Sentinel-1.

Open developer-friendly standard for querying satellite imagery

One of the things that is required to make the vast quantity of satellite imagery easily searchable is a common way to query satellite data.  The SpatioTemporal Asset Catalog, known as STAC, is an open specification that came about when fourteen different organizations came together to increase the interoperability of searching for satellite imagery. At this year's FOSS4GNA (Free and Open Source Software for Geospatial North America) get together in St Louis, Matt Hanson, of developmentSEED, gave a technical overview of the STAC standard and described one of the first implementations.  The context for Matt's STAC presentation was provided by Chris Holmes' keynote in the morning.

Chris Holmes and others have been working on a standard for searching satellite imagery. Currently when a user wants to search for all the imagery in their area and time of interest they can’t make just one search — they have to use different tools and connect to API’s that are similar but all slightly different. The STAC specification aims to make that much easier, by providing common metadata and API mechanics to search and access geospatial data. This and other standards are essential for opening up satellite data to processing and visualization.  This is one of a new breed of standards that are designed to be developer-friendly to encourage the open source community and others to get involved with the Open Geospatial Consortium (OGC) to further interoperability.

The STAC standard is minimalist, but balances that with extensibility.  Basically it is comprised of metadata, catalog, and API.  The core of STAC metadata is very simple, with only three mandatory fields; ID, geometry (for example, a bounding box in a defined projection), and date and time.  The standard supports extensions, of which the earth observation standard "eo" has been defined at this time.  Future extensions could include point cloud, mosaic and video extensions.  The eo extension includes parameters such as camera viewing angle, resolution (dist between pixels), percent cloud cover, and band descriptions such as RGB, SWIR, band frequency and band accuracy.

The STAC standard and cloud optimized GeoTiff (COG) files makes it possible to search and stream imagery. COGs are GeoTiff files optimized for the cloud, for example, by streaming them from Amazon Web Services S3.  Planet, Google Earth, and QGIS already support COGs and there are other open source tools such as COG-Explorer that support them as well.

Sat-api is developmentSEED's partial implementation of STAC.  It currently supports Landsat-8 and Sentinel-2 imagery.  The API allows these imagery libraries to be queried by any of the metadata fields such as date and time, location (by defining a polygon), bands, viewing angle, and so on. It returns GeoJSON.  Matt provided some example queries.

 

Find all Landsat-8 scenes from 2017 with 0-20% cloud cover

https://sat-api.developmentseed.org/search/stac?datetime=2017&collection=landsat-8&eo:cloud_cover=0/20

Find all scenes from Dec 31, 2016 through Jan  1, 2018

https://sat-api.developmentseed.org/search/stac?datetime=2016-12-31/2018-01-01

The next developments that developmentSEED is planning for the STAC standard is to add more data CBERS and MODIS and to develop some applications based on the API.

Enabling the rest of us to find actionable information in huge volumes of satellite data

There is a huge volume of satellite imagery that is being captured and has been captured over the past couple of decades.  The challenge is to automatically find objects such as ships, trees, buildings, or containers in this vast amount if data and track changes in them over time.  This morning at at this year's FOSS4GNA (Free and Open Source Software for Geospatial North America) get together in St Louis, Chris Holmes of Planet Labs, gave an insightful overview of the application of deep learning to geospatial data, identifying opportunities for the open source community to lead in the application of this data and technology to addressing this challenge.

Outside of Google, Planet Labs, DigitalGlobe and some others deep learning has been applied primarily to recognizing people in selfies and dogs and cats from peoples photos on online repositories such as Flickr. I have blogged previously about competitions DigitalGlobe (which has 100 petabytes of imagery) and Nvidia have hosted into automating building footprint and road network recognition. Planet Labs has a computer vision and analytics team that has been applying the results of academic research to geospatial data, primarily imagery from Planet's constellation of 150+ satellites. For example, they have used deep learning to detect building footprints and road networks in Dar es Salaam. Chris showed examples of airplane, ship and container recognition achieved using deep learning. But these are research pilots. One company has applied deep learning to scale for a practical application. Google has been developing and applying the technology to building recognition - automatically identifying building footprints and features such as aerials. The deep learning algorithms have been developed by academia and as a result the code for the most part is open source.  For example, a  deep neural network model developed originally for medical image segmentation called U-Net  has been applied to identifying building footprints.  Successful training of deep networks requires many thousand labeled training samples and this training data is not open source. Labeled data involves people on the ground manually ground-truthing building footprints and other features so that the deep learning algorithms can learn what to recognize.

Chris believed that it is time to open these models to a broader group. There are some technical things that need to happen to make this possible. We need new formats for exchanging raster and vector data. Cloud optimized GeoTiff (COG) makes it possible to stream imagery from cloud sources such as Amazon S3. Planet, Google Earth, and QGIS already support COGs and there are other open source tools such as COG-Explorer that support them as well. Chris and others are working on WFS 3.0, a completely new developer-friendly version of an existing OGC (Open Geospatial Consortium) standard for exchanging vector data over the web.

Another technical advance that is required is a common way to query satellite data based on date, time, location, type of bands, and so on.  Chris and others have been working on a standard for searching satellite imagery for objects. The SpatioTemporal Asset Catalog, known as STAC, is an open specification that came about when fourteen different organizations came together to increase the interoperability of searching for satellite imagery. Currently when a user wants to search for all the imagery in their area and time of interest they can’t make just one search — they have to use different tools and connect to API’s that are similar but all slightly different. The STAC specification aims to make that much easier, by providing common metadata and API mechanics to search and access geospatial data.  Chris emphasized that standards are essential for opening up satellite data to processing and visualization and encouraged the open source community to get involved with the OGC to further developer-friendly standards.

Currently in order to use imagery data, a lot of data preparation is required of the end user such as lining up pixels, assigning a projection, removing clouds and mist, and other corrections. We need to move toward analysis ready data where data derived from different sources can be combined for analysis.

Because of the huge volumes of computational intensity we need cloud-native engines to perform the analyses. Google, DigitalGlobe (Maxar), Planet have these deep learning engines, but Chris encouraged the open source community to get more involved in developing these engines.

An essential piece that is required to open up the data and technology to a broader audience is open labeled data, Chris referred to it as a "open labeled data commons". There are analogies with OpenStreetMap initiative. Chris suggested that something like this is required for labeled data - it needs a repository of open data, tools to capture and manage the data, and a community around this to capture and curate that data.

The other key piece is converting this data into actionable information.  For example, dashboards that would alert municipal governments to un-permitted development, forestry enforcement to illegal forest cutting, or commodity price monitoring agencies to changes into the flow of ships into harbours or to large changes in containers at container ports.

Taken together Chris suggestions and work underway represent a roadmap for the open source geospatial community to lead in making the huge volumes of satellite imagery and other data queryable and able to generate actionable information.

Deep learning enables automated extraction of building footprints and road networks from satellite imagery

Automated feature extraction from satellite imagery has made major progress in the last year. Accurate building footprints extracted from high resolution satellite imagery are available from companies such as Ecopia (which has just announced a partnership with DigitalGlobe).  Also NVIDIA has demonstrated the ability to automate detection of many road networks using sophisticated algorithms and multi-spectral high resolution imagery.

Building footprints

SpaceNet is a repository commercial satellite imagery and labeled training data being made available through Amazon Web Services at no cost to the public to foster innovation in the development of computer vision algorithms to automatically extract information from remote sensing data.  It is the result of a collaboration between DigitalGlobe, CosmiQ Works, and NVIDIA.  Two competitions for automated building footprint extraction from high resolution satellite imagery and a third for road network extraction have been held based on SpaceNet data. 

The first SpaceNet Challenge was launched in November 2016.  Imagery of Rio de Janeiro from the WorldView 2 satellite at 50cm resolution using eight spectral bands was made publicly available on Amazon Web Services (AWS).  42 developers competed to create algorithms that extract building footprints from the imagery.  The source code of the winning algorithms was made available on the SpaceNetChallenge GitHub repository. 

The winning implementation was developed by Brazilian developer using an implementation based on random forests with brute force polygon search and did not leverage deep learning frameworks.  The organizers' conclusion based on the results of Round 1 was that automated building footprint extraction remained a challenge.

Round 2 winner. Most building footprints are precisely detected. Number on each building footprint represents the overlap between the prediction and the ground truth.

Round 2 of the SpaceNet challenge involved higher resolution 30 cm imagery from WorldView 3 for four cities across the globe. The competition challenged developers to improve performance from the first competition using the higher-resolution imagery and more geographically diverse training data samples. SpaceNet on AWS held imagery and building footprints for Las Vegas, Paris, Shanghai, and Khartoum with over 180,000 buildings181,619 footprints.  In addition additional bands were made available (panchromatic, 3-band pan-sharpened, and 8-band pan-sharpened).

Results from Round 2 winning solution: top left: Las Vegas, top right: Las Vegas, lower right: Khartoum, and lower left: Paris. Blue outlines: ground truth, green outlines: correct identifications, red outlines: false positives and yellow outlines: false negatives.

The winner of Round 2 applied a  deep neural network model developed originally for medical image segmentation called U-Net.  Successful training of deep networks requires many thousand annotated training samples.  Training was aided significantly by augmenting the imagery with OpenStreetMap data.  The winning solution used OpenStreetMap layers and Worldview-2 multispectral layers as the input of the deep neural network algorithm.  In Las Vegas, the winning solution was outstanding in outlining suburban homes, but also did well in identifying odd-shaped buildings. In Khartoum, the solution was very good in identifying the footprints of stand-alone apartment complexes, but had trouble with small buildings, with building footprints that are close together, and with horizontally large buildings.  One of the problems was inconsistent ground truth which affected training. The solutions offered by the developers in the competition were sufficiently successful that the organizers concluded that the winning algorithms achieved performance with potential for automated mapping tasks such as keeping maps up-to-date and to assist first responders during natural disasters.  The source code for the winning implementations can be found in the SpaceNetChallenge GitHub repository.

DigitalGlobe has just announced a partnership with Ecopia, which has established an automated process to create building footprints quickly and at scale by leveraging DigitalGlobe's Geospatial Big Data platform (GBDX) and advanced machine learning in combination with DigitalGlobe’s imagery library. The two companies plan to automatically extract accurate 2D building footprints globally, then refresh the datasets periodically to find and track changes over time.  These datasets would be valuable to municipal governments for permitting purposes and for first responders after disasters.   Ecopia is developing a database for all of Australia (7.6 million square kilometers) which will include every building with a roof area greater than 9 square meters.  The database will contain building footprints for about 15 million buildings. The database includes linkages to other datasets including geocoded address, property data and administrative boundaries. Ecopia intend to update the dataset regularly to be able to track changes, especially urban sprawl.

Road networks

In November 2017 CosmiQ Works, Radiant Solutions, and NVIDIA announced a third round competition, “Road Detection and Routing Challenge” to explore automated methods for extracting map-ready road networks and routing information from high-resolution satellite imagery.  

Detecting road networks in Paris. Photo image, ground truth, and the results achieved with three deep learning algorithms

NVIDIA has released the results of several deep learning algorithms that illustrate just how difficult identifying road networks is and the sophistication of the available tools.  These include using multi-band spectral imagery to identify the material properties of the road surface itself (asphalt, gravel, packed earth) and using more sophisticated processing including conditional random fields (CRF), percolation theory, and reinforcement learning. 

Detecting road networks in Las Vegas. Photo image, ground truth, and the results achieved with three deep learning algorithms

NVIDIA utilized high performance GPU compute resources provided by the NVIDIA GPU Cloud and Amazon Web Services.

Geospatial imagery analytics market expected to grow rapidly

An very interesting report on the geospatial imagery analytics market in 2017 and its growth through 2022 has been released by MarketsandMarkets. The analysis looks at different types of imagery analytics (imagery analytics, video analytics), how the imagery is collected (GIS, satellites, UAVs), and how it is used by different vertical industries (defense & security, insurance, agriculture, healthcare & life sciences). The major players considered in the study include Hexagon AB, Harris Corporation, DigitalGlobe, ESRI, Google, Trimble, RMSI, Planet Labs, UrtheCast Corporation, Fugro N.V., KeyW Corporation, and Satellite Imaging Corporation.

The study estimates that the geospatial imagery analytics market was worth about US$ 3.41 billion in 2017. The video imagery segment is expected to grow at a higher rate than the image analytics segment. The study also sees a high growth rate for UAVs as a way of collecting imagery and video. Surprisingly for me, the healthcare and life sciences segment is projected to grow at the highest rate among different industry segments.  North America, followed by Asia and Europe, are expected to be the largest markets for imagery analytics in 2022.  MarketsandMarkets projects a high growth rate (31.1% CAGR) for geospatial imagery analytics reaching US$ 13.21 billion in 2022. Advances in artificial intelligence and big data are expected to be major contributors to this high growth rate. 

The analysis is based on a bottom-up approach by estimating the revenues of key market players. The data were verified through primary research by conducting extensive interviews with chief executive officers, vice presidents, directors, and other executives.

"Geospatial Imagery Analytics Market"

Geospatial technology innovation is transformative

Innovation in spatial technology is transforming some of the world’s most important economic sectors.  The world will need to spend $57 trillion on infrastructure through 2030 just to keep up with global GDP growth. To meet the world's future infrastructure needs, construction needs to be reinvented through a productivity revolution. Two spatial technologies, reality capture and building information modeling (BIM), are key to this transformation of construction. A series of two articles on the new spatial technologies that are transforming construction including hand-held scanners, single-photon laser scanning, SLAM for indoor navigation and autonomous UAVs, beyond visual line of sight (BVLOS) UAVs, satellite images with 30 cm (about a foot) resolution, and daily digital images of the entire Earth landmass from satellites is appearing in Damage Prevention Professional.