Each topic below explains the evidence, the business problem it creates, how GLOBEIR solves it, the benefits to your organisation, and how your data stays private and secure.
1. GIS-Based Master Plans under AMRUT and AMRUT 2.0
A GIS-based master plan starts with a geospatial database, not a drawing. Under the AMRUT sub-scheme, base layers are prepared from Very High Resolution Satellite (VHRS) imagery at 1:4,000 scale following the MoHUA Design & Standards of May 2016. The AMRUT 2.0 sub-scheme for Class-II towns adds drone/UAV mapping at 1:1,000 scale under the Design & Standards of October 2020 [13]. Existing land use, transport, water supply, sewerage, drainage, public amenities and environmentally sensitive areas are then held as linked layers, so planners can test proposals against what is actually on the ground [13].
Progress has moved well beyond the 2022 position. By August 2026, 461 AMRUT cities in 35 States/UTs had been onboarded, final geospatial databases were ready for 437 cities, draft GIS-based master plans had been prepared for 404 cities and 292 had been finalised. Under AMRUT 2.0, the sub-scheme covers 875 towns of 50,000 to 99,999 people. Draft databases were ready for 399 of these towns (100 finalised), and draft master plans for 136 (93 finalised) [13]. The geo-databases are also being integrated with PM GatiShakti: databases for 343 towns had been uploaded to the National Master Plan portal [13].
The Ministry is also clear about what a plan alone cannot do. Enforcement against unauthorised construction depends on regular updating of GIS data, integration with online building-permission and enforcement systems, and action by States and ULBs [13]. Capacity remains thin. About 2,968 officials had been trained under the AMRUT sub-scheme and 2,648 under AMRUT 2.0 [13]. That is a small number set against 1,336 cities and towns.
Business problem
- Many Class-II towns have neither a usable base map nor GIS staff, yet they must deliver a geospatial database and a draft plan to the national design standards before scheme timelines lapse.
- Once a database is approved, it begins to age. Without a routine for adding new buildings, roads and layouts, the plan is already out of date by the time it is notified, and its link to building permissions and enforcement weakens [13].
- States and development authorities must check large volumes of consultant deliverables for scale, classification, topology and attribute completeness, often without in-house tools to do so.
Our solution
- Base map production from VHRS imagery or drone surveys, with feature extraction to the layer list, scale and classification the client specifies, through our GIS mapping and cartography service.
- Digital micro-mapping of dense core areas and urban villages, where 1:4,000 imagery cannot resolve plots, lanes and drains.
- Quality-check scripts for topology, attribute completeness and classification codes, so authorities can review submissions consistently.
- A WebGIS workspace where planners view existing and proposed land use together, record objections against map locations, and export plan sheets.
- An update routine that feeds new imagery, permission records and field edits back into the master plan database between revisions.
Benefits
- One authoritative base map shared by planning, engineering and revenue teams, rather than separate departmental versions.
- Plans that fit national infrastructure planning: AMRUT geo-databases for 343 towns already sit on the PM GatiShakti portal (industry example) [13].
- Faster review of consultant deliverables because checks are automated and repeatable.
- A plan database that stays current, which the Ministry identifies as a condition for curbing unauthorised construction (industry example) [13].
Privacy & data security
- Data involved: very high-resolution imagery, drone orthophotos and building footprints, which together can reveal private premises at fine detail.
- Indian storage and processing: DST Guidelines require geospatial data finer than the threshold (1 m horizontal, 3 m vertical) to be created and owned by Indian entities and stored and processed in India [23]. Such data must never reach servers of a non-Indian entity [24]. GLOBEIR hosts on infrastructure in India or on the client's own servers.
- Public versus internal layers: published plan maps show land use and zoning only, while survey detail and ownership-linked attributes stay behind role-based access.
2. Property Tax GIS and Own-Source Revenue
Property tax GIS links three things that usually live apart: a map of every building, a unique property ID and the tax assessment record. The World Bank's review of Indian property taxation describes how this works in practice. Raipur obtained a new GIS base map, verified ward boundaries in the field, divided wards into equal blocks and gave every property a unique ID. Surveyors then used an online/offline mobile app preloaded with each ward's existing records to capture GPS coordinates, measurements and geotagged photographs, while supervisors watched progress in real time [6].
The measured gains come from closing coverage gaps. In Pune, GIS mapping and unique IDs raised the number of assessed properties by 18 percent, from 8.34 lakh to 9.23 lakh, and added ₹89 crore to an existing base of ₹228 crore [6]. Karnataka's Aasthi programme produced cadastral-level GIS maps covering over 3.8 million properties. It brought about 1.2 million previously unassessed properties (42 percent of the total) into the tax net, and revenue rose 30–40 percent [6]. In Visakhapatnam, revenue inspectors used tablets to spot under-assessed properties by comparing the GIS map with building permissions, occupancy certificates and trade licences [6].
The same review warns that most municipalities that mapped properties under JnNURM let the data lag. The reasons it gives are unclear cost-benefit, the cost of repeat surveys falling on the city, and imagery too coarse to settle disputes in dense areas [6]. Fiscal pressure now makes that lag expensive. The 15th Finance Commission made the notification of minimum floor rates for property tax by States, and improvement in property tax collection, an entry condition for grants to urban local bodies from 2021-22 onwards [18].
Business problem
- Grant eligibility now depends on rising property tax collections [18]. Yet many registers still miss new buildings, added floors and changes from residential to commercial use.
- Initial GIS surveys decay quickly when no process links building permissions, mutations and trade licences back to the map [6].
- Disputes over measured area and use slow collection when the city cannot show clear, dated evidence for each property.
Our solution
- Building footprint extraction from recent imagery, with unique property IDs assigned block by block.
- Door-to-door verification in the My GLOBEIR app: preloaded ward data, custom forms, geotagged photographs and offline capture, with supervisor monitoring through live tracking.
- Register matching that flags unassessed properties, under-reported floor area and changes of use, ranked by likely revenue impact.
- Ward-level coverage and collection dashboards on WebGIS, plus a change feed from new imagery and permission records so the register keeps pace with construction.
Benefits
- Wider coverage: Aasthi brought 1.2 million unassessed properties into the tax net (industry example) [6].
- Measurable revenue: Pune added ₹89 crore to its tax base from newly enumerated properties (industry example) [6].
- Evidence for every assessment, with dated photographs and measurements that reduce disputes.
- A register that stays current, which supports the 15th Finance Commission's requirement of steady improvement in collections [18].
Privacy & data security
- Data involved: owner names, contact details, property photographs and measurements linked to an exact location. This is personal data under the DPDP Act [19].
- Field app controls: surveyors see only their assigned ward; photographs are taken of the property, not of people; and owner details are stored apart from map layers.
- Masked analysis: coverage and revenue dashboards use property IDs and ward totals, with owner identity visible only to authorised revenue staff.
3. Land Use Change and Urban Growth Monitoring
Satellite archives make it possible to measure how a city has grown, not just describe it. An IISc study of Bengaluru Urban district classified Landsat imagery from 1973 to 2022 with a Random Forest machine learning model. It found a 51.86 percent increase in built-up area and a 26.28 percent decrease in green cover, including a loss of 177.2 sq km of native green cover in the south of the district [17]. Its Cellular Automata–Markov projection shows built-up area could reach about 1,536 sq km by 2038, with water bodies likely to disappear unless growth is planned [17].
Change detection matters most at the edge of the city. There, layouts and buildings appear faster than planning jurisdictions extend, and land records are being rebuilt from scratch. The NAKSHA pilot shows the model: Survey of India provides orthorectified aerial imagery, States carry out field surveys and ground-truthing, and an end-to-end web-GIS platform with NIC storage supports publication of urban land records. The pilot is fully centrally funded at about ₹194 crore [8].
Business problem
- Unauthorised layouts, encroachment on lakes and drains, and loss of farmland are usually noticed through complaints, after construction is complete and enforcement is costly.
- Development authorities cannot easily tell which detected structures already hold a permission, a tax assessment or a land record.
- Master plans project growth from census trends rather than observed change, so infrastructure and reserved land are planned in the wrong places.
Our solution
- Multi-date change detection on satellite and drone imagery, using GeoAI models to extract new buildings, extensions and land conversion.
- Long-run land use and land cover analysis through our remote sensing research service, with growth scenarios for plan revisions.
- Cross-checks of every detected change against permission, tax and land record layers, producing verification lists for field teams.
- Field verification in the My GLOBEIR app, with outcomes tracked on an administration monitoring dashboard.
Benefits
- Earlier detection of unauthorised construction and encroachment, while action is still practical.
- Inspection effort directed to verified change locations rather than random rounds.
- Evidence-based growth projections, of the kind the Bengaluru study produced for 2038 (industry example) [17].
- Land records and plans that stay aligned with what is on the ground after NAKSHA-style surveys [8].
Privacy & data security
- Data involved: time-series imagery of private property and the enforcement status of individual plots.
- Purpose limitation: change lists are used only for the agreed planning, revenue or enforcement purpose, and enforcement status is visible only to authorised officers.
- Indian hosting: fine-resolution imagery and derived layers are stored and processed in India, as DST Guidelines require [23].
4. Smart City ICCC Dashboards
An Integrated Command and Control Centre (ICCC) is intended to act as the "brain and nerve centre" of a smart city, with a decision support system for traffic, solid waste and water distribution [3]. All 100 smart cities have one. In 2023, 30 of them were working on traffic and transport through adaptive signal control, red light violation detection and automatic number plate recognition [3]. By March 2025, smart cities had installed over 84,000 CCTV cameras, 1,884 emergency call boxes and 3,000 public address systems, and ICCCs had served as COVID war rooms during the pandemic [2].
The map is what turns these feeds into decisions. A camera alert, a SCADA pressure drop or a missed waste collection becomes useful only when an operator can see which asset, ward and field team it relates to. Most ICCC platforms come with their own map. The ones that work well depend on a clean, maintained city base map and asset register behind them, and that layer is often missing or out of date.
Business problem
- Command centre screens show alerts on generic basemaps that do not match municipal ward boundaries, asset IDs or the latest road network.
- Departments keep separate asset lists, so an alert cannot be routed automatically to the team that owns the asset.
- With mission funding winding down, cities must show service outcomes by ward to justify operating costs. Raw feed counts do not do that.
Our solution
- An authoritative ICCC map layer of wards, roads, utilities, bins, streetlights and public facilities, published as standard web services through WebGIS so the existing ICCC software can consume it.
- Linking of complaints, sensor alerts and work orders to mapped assets, with field closure captured in the My GLOBEIR app and staff visibility through live tracking.
- Ward-level performance dashboards and heatmaps of recurring issues, through administration monitoring.
Benefits
- Faster routing of alerts to the right team because every alert is tied to an asset and ward.
- Recurring problem locations become visible, which supports planned rather than reactive maintenance.
- Outcome reporting by ward for councils and funding reviews.
- Better use of an installed base that already includes more than 84,000 cameras and SCADA on over 17,026 km of water network (industry example) [2].
Privacy & data security
- Data involved: CCTV footage, number plate reads, complaint records with names and phone numbers, and the locations of field staff. Images or records about an identifiable person are personal data under the DPDP Act [19].
- Separation of layers: GLOBEIR's map layers hold assets and aggregated events, not video. Footage and number plate data stay in the client's surveillance systems under the client's access rules.
- Incident readiness: logging and clock synchronisation are designed to support the client's CERT-In duties: 6-hour incident reporting and 180-day log retention in India [21].
5. Urban Flooding and Drainage
Urban flooding is a mapping problem as much as a rainfall problem. NDMA's guidelines record that when Mumbai received 944 mm of rain in 24 hours on 26 July 2005, over 60 percent of the city was inundated to some degree, and there was no real-time rainfall network to show where water was building up [10]. The guidelines call for:
- all 2,325 Class I, II and III cities and towns to be mapped on a GIS platform;
- automatic rain gauges at a density of one per 4 sq km;
- catchments to be the basis for drainage design;
- pre-monsoon desilting to be completed before 31 March each year [10].
They also identify encroachment on natural drains and floodplains as a major reason drainage capacity has fallen [10].
Funding is now tied to this kind of evidence. The 15th Finance Commission recommended ₹2,500 crore under the National Disaster Mitigation Fund for urban flood management in seven cities: ₹500 crore each for Mumbai, Chennai and Kolkata, and ₹250 crore each for Bengaluru, Hyderabad, Ahmedabad and Pune. A ₹561.29 crore project for the Chennai basin had been approved [14]. Under AMRUT, 750 storm water drainage projects worth ₹1,883 crore had been completed in 19 States/UTs, eliminating 3,445 waterlogging points [14].
Business problem
- Drain networks exist on paper drawings, if at all. Cities cannot see connectivity, outfall levels or which reaches are undersized for today's runoff.
- Desilting and capital works are prioritised from last year's complaints rather than from catchment analysis.
- Cities applying for flood mitigation funds need catchment-level evidence, hazard maps and a drain inventory to make the case [10][14].
Our solution
- A GIS storm water drain inventory, organised both by watershed and by ward as NDMA recommends [10], with field verification of sections, levels and outfalls in the My GLOBEIR app.
- Fine-resolution elevation models from drone or LiDAR surveys, catchment delineation and low-lying pocket analysis through our geospatial data science service.
- Overlay of past waterlogging points, rainfall and encroachment on natural channels to rank drain reaches for desilting and works.
- Monsoon-season dashboards linking waterlogging complaints and field crews on one map.
Benefits
- Drain works targeted to the catchments that cause waterlogging. AMRUT drainage projects eliminated 3,445 waterlogging points (industry example) [14].
- Stronger proposals for flood mitigation funding, backed by mapped evidence [14].
- Pre-monsoon desilting planned and verified reach by reach against NDMA's 31 March benchmark [10].
- Encroachments on natural drains documented for action.
Privacy & data security
- Data involved: high-resolution elevation and drainage data, and complaint records with residents' contact details.
- Aggregation: waterlogging analysis uses complaint locations without names or phone numbers.
- Indian storage: elevation data finer than the DST threshold (3 m vertical) is created by Indian entities and stored and processed in India [23].
6. Urban Heat and Green Cover
Heat varies sharply within a city, and satellites show where. The Delhi Heat Action Plan 2024-25 builds its thermal hotspot maps from Landsat 8 land surface temperature, overlaid on the city's ward boundaries to delineate wards above 42 °C [16]. On 30 May 2019, when Delhi recorded a maximum air temperature of 48 °C, the hottest zones showed land surface temperatures of 60.48 °C (Narela) and 59.06 °C (Najafgarh) [16]. The plan's mitigation measures include cool roofs on shelters and bus stands and pilot roof-whitening projects [16].
Green cover and heat are linked over time. The Bengaluru study cited above ties the loss of water bodies and green cover, which act as heat sinks, to rising land surface temperature and a changing microclimate [17]. Mapping both together lets a city decide where tree planting, cool roofs and shaded public spaces will reduce heat exposure most.
Business problem
- Heat action plans often treat a city as one unit, while exposure differs by tens of degrees of surface temperature between zones [16].
- Greening budgets go to available land rather than to the hottest, most built-up wards.
- Cities lack a baseline to show whether tree planting and cool roof programmes are working.
Our solution
- Ward-level land surface temperature, vegetation index and built-up density maps from Landsat and Sentinel data, through our remote sensing research service.
- Overlay of heat hotspots with schools, hospitals, bus stops, markets and outdoor work sites to show where people are exposed.
- Identification of public land, roofs and road verges suitable for planting or cool surfaces, published as heatmaps and WebGIS layers.
- Annual monitoring of tree cover and surface temperature to track programme results. See also our environmental monitoring guide.
Benefits
- Heat action targeted at the wards and facilities with the highest exposure, following the ward-overlay method Delhi uses (industry example) [16].
- Greening and cool roof spending prioritised by evidence.
- A repeatable satellite baseline for reporting change over time, without new field instruments.
Privacy & data security
- Data involved: satellite-derived surface data that contains no personal information, combined with facility locations.
- Place-based analysis: vulnerability is assessed by place and facility type, never by profiling individuals or communities.
- Health data: any heat-illness data supplied by health departments is used only in aggregated, ward-level form.
7. Underground Utilities, 3D City Models and Digital Twins
What lies below the road is the least mapped part of most Indian cities. Damage to buried assets from uncoordinated digging causes losses of about ₹3,000 crore every year. The Call Before u Dig (CBuD) app, launched in March 2023 under PM GatiShakti, connects excavators with asset owners for this reason. The aim is to reduce disruption to road, telecom, water, gas and electricity services [15]. The National Geospatial Policy 2022 sets a goal to survey and map sub-surface infrastructure in major cities and towns by 2035, alongside a National Digital Twin of major urban centres [12].
The same policy sets the data foundation for that twin. It targets high-resolution topographical surveys at 5–10 cm for urban and rural areas and a 25 cm Digital Elevation Model for the plains by 2030 [12]. A 3D city model adds building heights and forms to the 2D base map. A digital twin then links that model to live data: sensors, work orders and permissions. The model is only as useful as the asset register and base map beneath it.
Business problem
- Water, sewer, storm drain, power and telecom networks are recorded in separate drawings, many of them out of date, so excavation regularly damages other utilities [15].
- Road cutting is repeated because departments do not coordinate works on a shared map.
- Cities are offered 3D and digital twin platforms before they have a complete 2D asset register, so the twin has little reliable data behind it.
Our solution
- Consolidation of departmental utility drawings into one GIS asset register, with field verification and condition capture in the My GLOBEIR app.
- Integration of survey outputs such as ground-penetrating radar and utility locator results into the register, with depth and confidence attributes.
- 3D building models and terrain from drone photogrammetry and LiDAR through our 3D mapping and digital twins service, used for height controls, view analysis and flood depth visualisation.
- Phased digital twin delivery on WebGIS: the asset register first, then 3D, then live links to ICCC feeds and work orders. See also our power distribution and utilities guide.
Benefits
- Fewer utility strikes and service interruptions; national losses from uncoordinated digging are estimated at about ₹3,000 crore a year (industry example) [15].
- Coordinated road works across departments, reducing repeat cutting.
- A digital twin built in stages on verified data, aligned with the 2030 and 2035 national targets [12].
- 3D models that support building permission checks and flood and heat visualisation.
Privacy & data security
- Data involved: locations of critical utility networks and fine-resolution 3D models, which are security-sensitive even when they contain no personal data.
- Restricted access: utility layers are shared on a need-to-know basis, with role-based access, audit logs and no public publication of critical network detail.
- Indian entities and storage: DST Guidelines allow a negative list of sensitive attributes to be regulated and require fine-resolution data to stay in India [23]. A 2022 clarification adds that such data must never reach servers of any non-Indian entity [24]. Air-gapped or on-premise deployment is available where required.