Agricultural production has been among the most important drivers of the Indian economy for over a century. 70% of the Indian population relies on land holdings (both agricultural and commercial) for a living. While urban plots are cared for and records are kept, the same cannot be said for rural property. The great majority of landholdings wind up in the family or judicial conflicts due to a lack of accurate and verifiable land record data. Prime Minister Narendra Modi established the SVAMITVA initiative to address rural India's century-old land ownership crisis.
The British coined the idea of land record data to gather taxes (lagaan). Residential and commercial lands were never acknowledged in these records, which were exclusively kept for agricultural plots.
The rural population of India increased dramatically after colonial rule. As a result, as families got larger, so did each member's claim to their land. The mechanism for keeping land records, on the other hand, still hadn't evolved. Rather than employing current technologies, this system depended on village authorities (Tehsildars) to maintain substantial records holding easily manipulatable local reference material. Because this approach was prone to errors, land disputes grew more frequent. When a matter was tried in court, the issue arose. Due to the absence of legitimacy, both the judiciary and banks refused to accept land register data as proof of ownership. As a result, families lost their native homelands as well as their source of income. Families also had no access to banking services for the same reason. A farmer could not mortgage his land for credit if he did not have any formally recognized ownership rights.
WHAT IS SVAMITVA?
The Survey of Villages Abadi and Mapping with Improvised Technology in Village Area (SVAMITVA) scheme was initiated on April 24th, 2020, to provide accurate property record data utilizing drone technology. By providing a property card, SVAMITVA hopes to provide people with irrefutable property rights. This property card would include the owner's contact information as well as the precise perimeter dimensions of their land. Property owners can use this property card not just to resolve property disputes lawfully, but also to obtain financial services by taking out a loan on their land.
SVAMITVA, at its foundation, will end the century-old land dispute. Tehsildars and physical land record registers will be obsolete. Traditional register entries will be replaced with centimeter-accurate geographic locations that can be digitally validated in minutes. Rural residents' children will be able to pay for education and engage in the service industry as they acquire access to banking services. This will liberalize rural youth, raise living standards, and develop a new workforce in the country. Furthermore, this will contribute to the creation of a massive database that organizations may use to provide crucial emergency services, e-commerce, and logistic services to the rural populace for the very first time.
Once the government begins to recognize proprietors and their holdings through property cards, their total wealth grows on record. This dramatically improves the aggregate worth of rural assets, hence increasing the country's overall wealth. On paper, growing wealth has yet another flowing benefit for the economy in the form of Foreign Direct Investments (FDIs). A stronger economy will entice significant investments, particularly in rural India.
HOW DOES IT WORK?
This scheme takes advantage of drone technology. Survey-grade drones are being used to map India's 6.2 lakh villages and develop high-resolution orthographic digital maps of each hamlet. Every parcel of land is delineated using centimeter-accurate geographic coordinates. These coordinate markers are subsequently registered and noted on a property card supplied to the landowner. SVAMITVA is being implemented by the Survey of India (SOI) in collaboration with Aarav Unmanned Systems (AUS), a commercial drone manufacturing business. AUS is the only private organisation chosen to deploy survey-grade drones for mapping purposes.
To begin, SOI develops a CORS (Continuously Operating Reference Station) Network in the mapped areas. A CORS network is a network of RTK base stations that transmit coordinate corrections against GNSS satellites to achieve maximum accuracy. Survey-grade drones outfitted with PPK modules use rectified data from these CORS networks to geotag each acquired image with precise global coordinates. SOI creates various networks in stages. SOI has presently constructed CORS circuits across Uttar Pradesh, Rajasthan, Maharashtra, Madhya Pradesh, and Karnataka in stage one.
When a village's CORS system is constructed, state authorities initiate the marking procedure. The officials use choona or calcium hydroxide to define the perimeter of each land with a prominent 'L' or 'T'. This functions as the drones' GCP (Ground Control Point). GCPs are utilized or precision by drone image processing techniques.
The next day, a crew of SOI and AUS drone operators arrives at the site and begins flying the drones to collect aerial data. The drone takes numerous photographs while flying independently based on a flight path. A vertical overlap of 70% and a horizontal overlap of 60% exists between two successive photos. The resolution of each georeferenced image is such that when zoomed in, the choona inscriptions on the land boundaries may be easily identified. After collecting the aerial data, it is forwarded to SOI for processing. The image of the land, as well as the specific measurements, are displayed on a property card. The property card is given to state officials, who then distribute it to the appropriate landowners.
Drone mechanics has come to the forefront in several industries in India. From BVLOS drone-based delivery experiments to the largest global drone mapping project, we've come a long way. SVAMITVA is the first of its type and has enormous potential. Apart from the land registry, the high-utilresolution images generated from aerial data have a wide range of applications. The data can be utilized to build infrastructure such as sewage pipelines, fibernet lines, school and hospital inspections, and so on. Using machine learning to examine these models can greatly improve the efficiency of infrastructure design.
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