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According to Engadget in March 2017, there are over 770,000 drone owners registered to fly in the US. That’s up from 670,000 at the beginning of 2017, meaning 100,000 users signed up in just three months alone. The FAA has also issued 37,000 Remote Pilot Certificates that let drone owners do the filming, inspection and other commercial operations. So, it’s not only our roads that are congested.
The growing popularity of drones, whether for leisure or commercial use, has highlighted the challenge of facilitating traffic in very-low- altitude airspace. As they are airborne objects, drones fall under aviation law. However, that’s only part of the challenge for drone flyers. Because they fly in the low level airspace, drones also need to take into account obstacles, buildings and people’s privacy.
(Image Source: https://360.here.com)
For autonomous drones to operate safely and predictably, access to rich and accurate data sources is key. Standards to support interoperability, just like those practiced by the aviation industry, are also needed. To meet these needs, they HERE is teaming up with UNIFLY, the Unmanned Traffic Management (UTM) platform, to develop 3D airspace maps for drones.
In the first phase of their collaboration, the companies plan to enable an airspace map for drones that covers both rural and urban areas, and marks out no-fly zones, such as airports, residential areas and sensitive government installations.
In the second phase, the companies plan to further develop the system to support the management of drone traffic flow and even collision avoidance, much like air traffic controllers do for the airline industry today. Longer-term, the aim is to explore how drone transportation and logistics can be integrated seamlessly into the broader transportation system.
The Unifly UTM platform connects relevant local and aviation authorities with drone pilots to safely integrate drones into the airspace. HERE, meanwhile, is developing the Reality Index™, a rich real-time digital representation of the physical world. Based on the companies’ commercial agreement, Unifly will integrate HERE map and location data from the Reality Index™ into its applications to provide a more and more robust picture of the low-altitude airspace.
Drones: the ultimate users of the Reality Index™
A drone generally needs a map from the ground up to an altitude of about 150 meters; in future, a flying taxi may need the map to extend higher. Drones need to take into account obstacles, buildings and people’s privacy. As airborne objects, they are also subject to various airspace regulations.
(A 3D visualization of the world, Image Source: https://360.here.com)
For drones to operate safely and predictably, access to rich and accurate data sources is paramount. These data sources must also be kept updated to ensure usefulness. Just as HERE today turns the real-time sensor data generated by millions of vehicles on the road into map information and new location services for drivers and passengers, drones themselves could also be employed to enable the self-healing of the airspace map. Equipped with various sophisticated sensors, drones could detect changes in the real-world environment and feed data back to the cloud to support map updates.
By aggregating data from many drones, the airspace map could also be enriched with precise information about hyperlocal weather conditions, potential hazards and the best navigable routes.
New technologies such as Artificial Intelligence (AI), Cloud Machine Learning, Satellite Imagery and advanced analytics are empowering small-holder farmers in India to increase their income through higher crop yield and greater price control, Microsoft India said.
(Photo Source: ICRISAT)
In a few dozen villages in Telengana, Maharashtra and Madhya Pradesh, farmers are receiving automated voice calls that tell them whether their cotton crops are at risk of a pest attack, based on weather conditions and crop stage. In Karnataka, the government can get price forecasts for essential commodities such as tur (split red gram) three months in advance for planning the Minimum Support Price (MSP).
“Sowing date as such is very critical to ensure that farmers harvest a good crop. And if it fails, it results in loss as a lot of costs are incurred for seeds, as well as the fertilizer applications,” Suhas P. Wani, Director, Asia Region, of the International Crop Research Institute for the Semi-Arid Tropics (ICRISAT), said in a Microsoft blog post.
The non-profit ICRISAT conducts agricultural research for development in Asia and sub-Saharan Africa with a wide array of partners throughout the world. In collaboration with ICRISAT, Microsoft has developed an AI-Sowing App powered by Microsoft Cortana Intelligence Suite including Machine Learning and Power BI.
“The app sends sowing advisories to participating farmers on the optimal date to sow. The best part – the farmers don’t need to install any sensors in their fields or incur any capital expenditure. All they need is a feature phone capable of receiving text messages,” the company said.
To calculate the crop-sowing period, historic climate data spanning over 30 years – from 1986 to 2015 – for the Devanakonda area in Andhra Pradesh was analysed using AI. To determine the optimal sowing period, the Moisture Adequacy Index (MAI) was calculated. MAI is the standardised measure used for assessing the degree of adequacy of rainfall and soil moisture to meet the potential water requirement of crops. This data is then downscaled to build predictability and guide farmers to pick the ideal sowing week.
This year, ICRISAT has scaled sowing insights to 4,000 farmers across Andhra Pradesh and Karnataka for the Kharif crop cycle (rainy season). Predictive analysis in agriculture is not limited to crop growing alone. The Karnataka government will start using price forecasting for agricultural commodities, in addition to sowing advisories for farmers in the state. Commodity prices for items such as tur, of which Karnataka is the second largest producer, will be predicted three months in advance for major markets in the state, Microsoft said.
Microsoft has developed a multivariate agricultural commodity price forecasting model to predict future commodity arrival and the corresponding prices. The model uses remote sensing data from geo-stationary satellite images to predict crop yields through every stage of farming. The model currently being used to predict the prices of tur, is scalable, and time efficient and can be generalised to many other regions and crops.
India has the highest net cropland area while South Asia and Europe are considered agricultural capitals of the world. A new map was released on 14th November 2017, detailing croplands worldwide in the highest resolution yet, helping to ensure global food and water security in a sustainable way.
The map establishes that there are 1.87 billion hectares of croplands in the world, which is 15 to 20 percent—or 250 to 350 million hectares (Mha)—higher than former assessments. The change is due to the more detailed understanding of large areas that were never mapped before or were inaccurately mapped as non-croplands.
Earlier studies showed either China or the United States as having the highest net cropland area, but this study shows that India ranks first, with 179.8 Mha (9.6 percent of the global net cropland area). Second is the United States with 167.8 Mha (8.9 percent), China with 165.2 Mha (8.8 percent) and Russia with 155.8 Mha (8.3 percent). Statistics of every country in the world can be viewed on an interactive map.
(This map shows cropland distribution across the world in a nominal 30-meter resolution. This is the baseline product of the GFSAD30 Project. Source: USGS)
South Asia and Europe can be considered agricultural capitals of the world due to the percentage of croplands of the total geographic area. Croplands make up more than 80 percent of Moldova, San Marino and Hungary; between 70 and 80 percent of Denmark, Ukraine, Ireland and Bangladesh; and 60 to 70 percent of the Netherlands, United Kingdom, Spain, Lithuania, Poland, Gaza Strip, Czech Republic, Italy and India. For comparison, the United States and China each have 18 percent croplands.
The study was led by the USGS and is part of the Global Food Security-Support Analysis Data @ 30-m (GFSAD30) Project. The map is built primarily from Landsat satellite imagery with 30-meter resolution, which is the highest spatial resolution of any global agricultural dataset.
Importance of Monitoring Croplands in Great Detail
“The map clearly shows individual farm fields, big or small, at any location in the world,” said Prasad Thenkabail, USGS research geographer and Principal Investigator for the GFSAD30 Project Team. “Given the high resolution of 30 meters and 0.09 hectares per pixel, a big advantage is the ability to see croplands in any country and sub-national regions, including states, provinces, districts, counties and villages.”
With the global population nearing the 7.6 billion mark and expected to reach 10 billion by 2050, it is of increasing importance to understand and monitor the state of agriculture across the world in great detail. This new research is useful to international development organizations, farmers, decision-makers, scientists and national security professionals.
“This map is a baseline and starting point for higher level assessments, such as identifying which crops are present and where, when they grow, their productivity, if lands are left fallow and whether the water source is irrigated or rain fed,” said Thenkabail. “Comparisons can be made between the present and past years as well as between one farm and another. It is invaluable to know the precise location of croplands and their dynamics to lead to informed and productive farm management.”
Critical for Water Security
Not only does this map and accompanying data have significant food security implications, but it is also critical as a baseline for assessing water security. Nearly 80 percent of all human water use across the world goes towards producing food, and this research provides insight on “crop per drop,” which is an assessment of the number of crops produced per unit of water.
Download data through the Land Processes Distributed Active Archive Center.
A new datum or geospatial reference system is being introduced in the United States to become the official datum in 2022. At GIS in the Rockies, Pam Fromhertz of the NOAA National Geodetic Survey gave an overview of the reasoning behind the new datum, technical details about the change and some practical implications.
Most people in the geospatial sector in the U.S. are aware of the datums NAD27 and NAD83 which have been the reference points for all surveys performed in the U.S. NAD83 was defined primarily using terrestrial surveying techniques. NAD83 has been updated several times since being introduced in 1983 but is based on an ellipsoid that is non-geocentric and is tilted slightly. The new datum or North American Terrestrial Reference Frame of 2022 (NATRF2022) is based on gravity which means that “sea level” is now represented by an equipotential gravity surface rather than the Earth’s ellipsoid. The new reference frames will rely primarily on Global Navigation Satellite Systems (GNSS) such as the Global Positioning System (GPS) as well as an updated and time-tracked geoid model. Importantly, the new datum means that Mexico, Canada, and the U.S. will share a common datum. The gravity-based vertical datum will be accurate at the 2 cm level for much of the U.S. Gravity data is currently being captured across the U.S. and its territories as part of the Grav-D project.
Practically, this means that elevations may change by up to a meter and horizontal location by up to 1.5 meters. The actual corrections to elevations and horizontal locations will depend on where you are in North America. The greatest changes are in the Pacific Northwest and the least in the southeastern U.S. At the hotel in Inverness, Colorado where the GIS in the Rockies conference took place this year, the corrections were 1.36 m horizontally and -0.67 m vertically. The NOAA National Geodetic Survey web site (geodesy.noaa.gov) has tools to perform conversions from NAD83 to the new 2022 datum.
North Eastern Space Applications Centre (NESAC) has been providing thunderstorm nowcasting (forecasting up to 4 hours) services for North Eastern Region (NER) of India since 2015 under the North Eastern Regional node for Disaster Risk Reduction (NER-DRR) initiatives. This was done using the data from satellite imager and sounder onboard INSAT-3D / INSAT-3DR, automatic weather station data, and by analysing numerical weather forecast data. However, it was difficult to detect, track and forecast using this data alone as most of the thunderstorms being a localised event, extending only over a few tens of km and having a lifetime of less than one hour. The availability of DWR data has opened a new window for precise identification of thunderstorm weather systems, track them and forecast the probable areas which may get affected, albeit with lesser lead time.
The first S-band dual-polarimetric Doppler Weather Radar (DWR) was installed at Cherrapunjee, Meghalaya which was dedicated to the nation by Shri Narendra Modi, Hon’ble Prime Minister of India on May 27, 2016. NESAC is operating the DWR continuously since then, and the data is made available in near real-time for the public through the MOSDAC (Meteorological and Oceanographic data archival centre) and IMD websites. The DWR is calibrated at regular intervals and the data and products are being validated. It has unobstructed coverage for the entire state of Meghalaya, Tripura, Southern Assam, and part of Mizoram and Manipur. For the western and central Assam region, the DWR has coverage beyond 3-degree elevation only. The DWR also sees a large part of India’s neighbouring country, Bangladesh. The radar completes one volume scan in 11 minutes, comprising of 360-degree azimuth scan for 10 elevation angles ranging from 0.5 to 21 degrees. It also allows sector scan (in both azimuth and elevation) for high temporal observation of any event. The DWR covers a distance of 250 km (up to 500 km only for Z) with a spatial resolution of 300 m.
A thunderstorm is a pre-monsoon season (April-May) phenomenon over the NER of India. The data collected by the DWR during 2016 was used to understand the thunderstorm and storm signatures and calibrate the nowcasting model. During 2017 the nowcasting service was made operational. Severe thunderstorm nowcasting services for Southern Assam, Meghalaya, and Tripura were done primarily using the DWR data and for the rest of the NER, the earlier methodology was used. In addition to the Z (radar reflectivity), S (spectral width) and V (velocity) data collected by the DWR, extensive use of the polarimetric data like ZDR (differential reflectivity) and ρHV (Correlation coefficient) were also made to differentiate thunderstorm clouds from non-thunderstorm clouds.
The use of the Cherrapunjee DWR data has improved the thunderstorm nowcasting accuracy over Meghalaya, Southern Assam, and Tripura states. Altogether 48 severe and very severe thunderstorms were forecasted in these three states during April 1 to June 15, 2017, period. The accuracy of nowcasting was more than 90% with lead time varying from 30 minutes to more than 2 hours. The nowcasting services were disseminated through NER-DRR website and also through direct communication to the concerned at the state level.
(The DWR, Cherrapunjee coverage for an elevation angle of 3 degrees (left). Calibration of the DWR using metal sphere attached to hydrogen gas-filled balloon & Pisharoty sonde (right))
(Max V (left) and Max S (right) data from DWR, Cherrapunjee. Max V is used to estimate the velocity at which a weather system is moving and Max S gives an idea about the internal turbulence within cloud system)
High-resolution optical imaging Earth Observation Satellite (EOS) systems such as CARTOSAT provide multi-spectral remote sensing data in the visible and near-infrared (VNIR) wavelengths of the order of sub-meter to few-meters. These datasets can be used in a variety of applications, particularly associated with precise mapping, monitoring and change detection of earth’s surface, if top of the atmosphere (TOA) measurements can be properly compensated for atmospheric absorption and scattering effects. Existing physics based atmospheric correction (AC) algorithms for multi/hyperspectral remote sensing data over land involves simultaneous use of visible and short-wave infrared (SWIR) channels to derive aerosol information. Hence, such algorithms cannot be used for AC of data acquired by VNIR sensors to derive “surface reflectance”.
Towards this, Space Applications Centre, Ahmedabad has developed a new algorithm for AC of high-resolution VNIR remote sensing data in which aerosol information is retrieved from sensor measurements in VNIR channels and by selecting appropriate aerosol optical properties from a set of defined aerosol models. The algorithm uses lookup tables generated with vector radiative transfer calculations. Derived aerosol information and pre-computed lookup tables are employed to derive surface reflectance. Good quality surface reflectances have been obtained when this algorithm was applied on Cartosat-2 Series Satellite data. It is found that this algorithm significantly removes the haze from the images, making surface features distinctly visible, and hence more useable for qualitative as well as quantitative analysis and further applications.
Following figures illustrate the drastically improved quality of the images after applying the AC algorithms, where the contribution of light due to molecular scattering and scattering from thick layer of aerosol to the sensor measurement at the top the of the atmosphere is removed.
(Parts of Ahmedabad as viewed from Cartosat-2 Series Satellite on 03/11/2016)
(Cartosat-2 Series Satellite View of Ahmedabad, Satellite Area on 03/11/2016)
Brandon Jarratt took GIS professionals behind the scenes of animated city creation at the Esri User Conference, being held this week in San Diego. Jarratt served as general technical director for Disney’s Zootopia, which won the 2016 Academy Award for Best Animated Feature Film. Jarrett took the stage during the plenary session to describe how the Zootopia team used Esri CityEngine software to create the complex city that serves as the backdrop for the movie.
Jarratt said Disney animated features need three elements: compelling stories, appealing characters, and believable worlds. That’s believable worlds, not realistic worlds.
In this case, the complex city of Zootopia had to be designed from the ground up as a complex city with various districts designed to accommodate the vast array of animal species. In the world of Zootopia, humans don’t exist. Transportation systems, houses, streets, and services need to accommodate animals as tall as giraffes and as small as a shrew. To meet these challenges, the designers turned to Esri CityEngine and its multi-scaling feature. The Zootopia world also needed to incorporate various habitats, or in this case, districts. At the centre a large complex city dominates.
CityEngine was used in the creation of the city in Big Hero 6 as well. In Big Hero 6, the base city geography used was San Francisco, upon which Japanese-style buildings were placed. In all, 80,000 buildings were incorporated into San Fransokyo.
(San Fransokyo in Big Hero 6. (Image: Disney))
Zootopia, on the other hand, was built from scratch – including the terrain. The team started with research of various landscapes to create a basemap.
(Zootopia concept map. (Photo: T. Cozzens))
At the city-building stage, CityEngine’s custom tool was used to lay down streets. Buildings were designed for each district. The building styles couldn’t be repeated too often, or the city would look unrealistic, Jarratt said. The designers used carefully calibrated mix rules to keep the cities lively.
(The desert area of Sahara Square is made of 61,000 parts, including buildings, wall segments and palm trees. (Image: Disney))
The ability in CityEngine to change the makeup of a city, adjusting the frequency of the various parts, made it easy for the illustration team to meet the art director’s requirements. When he wanted more skyscrapers or buildings of a certain design, the team was able to provide new concept images the same day.
(Zooptopia being built in Esri CityEngine. (Photo: T. Cozzens))
Esri’s CityEngine GIS technology is used by city planners to design our future smart cities. “It’s so similar to how city planners create real cities,” said Esri President Jack Dangermond. He then presented Jarratt with Esri’s first-ever Best Animated Feature Using GIS award.