Month: January 2018
India’s Polar Satellite Launch Vehicle, in its 42nd flight (PSLV-C40), has launched the 710 kg Cartosat-2 Series Satellite for earth observation and 30 co-passenger satellites together weighing about 613 kg at lift-off. PSLV-C40 was launched from the First Launch Pad (FLP) of Satish Dhawan Space Centre (SDSC) SHAR, Sriharikota. In its first mission of 2018, the Indian Space Research Organisation (ISRO) successfully launched its 100th satellite. The mission comes a little over four months after the space agency’s unsuccessful launch of IRNSS-1H. Prime Minister Narendra Modi, congratulating ISRO for its success, said the launch signifies the bright future of India’s space programme.
(PSLV-C40 on First Launch Pad – Evening View. Source: ISRO)
The co-passenger satellites comprise one Microsatellite and one Nanosatellite from India as well as 3 Microsatellites and 25 Nanosatellites from six countries, namely, Canada, Finland, France, Republic of Korea, UK and USA. The total weight of all the 31 satellites carried onboard PSLV-C40 is about 1323 kg.
The 28 International customer satellites are being launched as part of the commercial arrangements between Antrix Corporation Limited (Antrix), a Government of India company under Department of Space (DOS), the commercial arm of ISRO and the International customers.
Cartosat-2 Series Satellite is the primary satellite carried by PSLV-C40. This remote sensing satellite is similar in configuration to earlier satellites in the series and is intended to augment data services to the users.
The imagery sent by satellite will be useful for cartographic applications, urban and rural applications, coastal land use and regulation, utility management like road network monitoring, water distribution, creation of land use maps, change detection to bring out geographical and manmade features and various other Land Information System (LIS) as well as Geographical Information System (GIS) applications.
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.