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. Research Data Acquisition and Processing
Indian researchers now have more free Earth observation data than ever before. NRSC's Bhoonidhi portal disseminates open and priced satellite data products, and Bhuvan, ISRO's geoportal, lets users visualise imagery and download and analyse thematic maps of India [7]. Under the Indian Space Policy 2023, IRS data coarser than 5 m is open to all users on Bhoonidhi, including archives from Resourcesat, Oceansat and EOS-04 ScanSAR alongside Landsat-8 and 9 and Sentinel-1 and 2; data finer than 5 m is free to government entities and priced for others [8].
International archives add depth. The vast majority of Copernicus data, including the Sentinel missions, is available to any organisation worldwide on a free, full and open basis, and can be downloaded or processed directly in the cloud [9]. When USGS made Landsat data free over the internet in 2008, downloads rose substantially and science applications expanded rapidly, and the policy has since shaped similar policies worldwide, including Copernicus [10]. Google Earth Engine remains free of charge for students, faculty and staff using it for academic research or teaching [11].
Policy has also removed earlier barriers. DST's 2021 geospatial guidelines apply explicitly to academic and research institutions and state that, except where specifically provided, no prior approval, security clearance or licence is needed to collect, prepare, publish or store geospatial data within India [2]. They also require publicly funded geospatial data to be made easily accessible for scientific purposes [2].
Business problem
The constraint has moved from obtaining data to preparing it. A doctoral scholar studying cropping patterns, urban growth or glacier retreat may need hundreds of scenes across several sensors, each requiring cloud masking, compositing, reprojection and index calculation. Done by hand, this can consume a large share of a fixed-length fellowship, and undocumented steps make it hard to reproduce or extend the work later.
Our solution
- Data scoping with the principal investigator: which sensors, resolution, period and variables the research question actually needs, and whether free data from Bhoonidhi, Bhuvan, Copernicus or Landsat is sufficient [8][9][10].
- Scripted pre-processing in Google Earth Engine or open-source desktop tools: cloud masking, seasonal composites, spectral indices, digital elevation model derivatives and clipping to the study area.
- Analysis-ready datasets delivered in open formats, together with the scripts and a processing note the scholar can adapt for the methods chapter.
- Support for combining satellite data with official boundaries and thematic layers through our remote sensing research services.
Benefits
- Researchers spend their time on the scientific question rather than on repetitive pre-processing.
- Scripts and processing notes make the data chain transparent to supervisors, examiners and reviewers.
- Industry example: the opening of the Landsat archive in 2008 led to a substantial increase in downloads and a rapid expansion of science applications, showing what open data enables when it is put to use [10].
- Free data and free academic cloud processing keep project costs low [8][11].
Privacy & data security
- Medium-resolution satellite imagery and public thematic layers do not normally contain personal data, but study area files, sampling plans and unpublished results are confidential research assets and are kept within the project's access group.
- High-resolution data finer than the DST threshold must be stored and processed in India under the 2021 guidelines; we host such data only on India-based infrastructure or the institution's own servers [2].
- Licence terms of priced or restricted datasets are respected and data is used only for the agreed project.
2. Field Data Collection with Mobile GIS
Most strong remote sensing and environmental studies rest on ground data: training and validation points for classification, soil, water and vegetation samples, infrastructure inventories, or household and facility surveys. India's Ph.D. enrolment rose 81.2 per cent between 2014-15 and 2021-22 to 2.13 lakh [6], and a growing share of these scholars and their supervisors run field campaigns each season.
Mobile GIS replaces paper forms and separate handheld GPS devices with a single app that captures the location, time, photos and structured attributes of each observation. Where surveys involve people, ICMR's 2017 national ethical guidelines allow informed consent to be administered and documented using electronic systems, provided the information conveyed matches the written consent document [13].
Business problem
Field seasons are short and expensive. Paper forms and GPS readings copied by hand produce transcription errors, missing coordinates and photos that cannot be matched to samples. Supervisors often discover gaps only after the team has returned, when revisiting remote sites is no longer affordable. Inconsistent forms across seasons or field assistants make the dataset hard to combine and share.
Our solution
- The My GLOBEIR app configured with the project's own forms: sample IDs, dropdowns, validation rules, mandatory photos and GPS capture for each record.
- Sampling designs or target points loaded onto the app so field teams can navigate to planned locations, with offline working in areas without connectivity.
- A WebMap dashboard for supervisors to check daily coverage and data quality during the field season.
- Electronic consent screens for surveys involving people, set up to match the protocol approved by the institution's ethics committee [13].
- Clean exports in open formats (CSV, GeoPackage, shapefile) for analysis, see our mobile GIS services.
Benefits
- Each observation is geo-referenced and time-stamped at the point of capture, removing transcription steps.
- Supervisors can spot missing samples or quality problems while the team is still in the field.
- Consistent digital forms make multi-season and multi-site datasets comparable.
- Electronic consent and structured records support the documentation that ethics committees expect [13].
Privacy & data security
- Household locations, names and responses from people are personal data under the DPDP Act, and location traces and geotagged photos of identifiable individuals are covered [14]. Forms collect only what the approved protocol needs.
- Identifiers and exact household coordinates can be stored separately from survey responses, with access limited to named investigators, and outputs published only in aggregated or displaced form, so that no participant can be identified [13].
- Location tracking of field staff in the My GLOBEIR app is optional and opt-in with a persistent notification, and is used for work purposes only; offline records sync securely when connectivity returns.
- Data is encrypted in transit (TLS 1.2 or higher) and at rest (AES-256).
3. Spatial Analysis, Modelling and GeoAI
Spatial analysis turns maps into evidence: detecting land use change, finding statistically significant hotspots, interpolating field measurements, modelling watersheds and terrain, measuring access to services or classifying imagery with machine learning. The National Geospatial Policy 2022 explicitly promotes cutting-edge research in geospatial science and technology and research that integrates geospatial technology with emerging technologies [1].
Cloud platforms have made large-scale analysis practical for academic groups. Earth Engine's free access for academic research and teaching means a department without a computing cluster can run classification or time-series analysis over a whole state [11]. The scientific risk lies less in computing power than in method choice, sampling and validation.
Business problem
Many research teams in agriculture, hydrology, public health, economics or civil engineering have a strong spatial question but no in-house specialist to select and validate the right method. Common problems include classification accuracy reported on training data, interpolation without cross-validation, spatial autocorrelation ignored in regression, and models that cannot be explained in a viva or rebuttal letter.
Our solution
- A method review with the research team to match the question to suitable techniques, such as change detection, spatial statistics, interpolation, terrain and hydrological analysis, network and accessibility analysis, or suitability modelling.
- Implementation in open-source Python or R and Earth Engine, with parameters, assumptions and sensitivity checks recorded through our geospatial data science services.
- GeoAI classification and prediction with properly separated training and validation samples and full accuracy reporting, through our AI and machine learning services.
- Walk-through sessions so the scholar understands, owns and can defend every analytical step.
Benefits
- Methods chosen to fit the research question rather than the tools at hand.
- Transparent accuracy and uncertainty reporting that stands up to peer review.
- Large-area analysis without a local computing cluster, using free academic cloud access [11].
- Scripts that later students in the same laboratory can reuse and extend.
Privacy & data security
- Where analysis uses survey or health-related data about people, it runs on pseudonymised or aggregated data, and sensitive information is protected to avoid stigmatisation, as ICMR's guidelines require [13].
- Research data is never used to train models for other clients or projects.
- Role-based access and audit logs record who accessed project datasets and results.
4. Cartography and Publication-Quality Maps
Maps are often the figures that reviewers and readers look at first. A good research map uses an appropriate projection and datum, a clear scale and legend, readable labels, colour schemes that remain distinguishable for colour-blind readers and in greyscale, and boundaries consistent with official sources. The National Geospatial Policy 2022 treats Survey of India topographic data and other geospatial data produced using public funds as a common good to be made easily available [1].
DST's 2021 guidelines removed the need for prior approval to prepare and publish maps within India, and they apply to academic and research institutions [2]. This places the responsibility for accuracy and presentation squarely with the author.
Business problem
Manuscripts are frequently returned for map revisions: missing scale bars, unreadable legends, inconsistent symbology across panels, low-resolution exports or boundaries that do not follow official sources. Researchers who are experts in their subject but not in cartography lose weeks at revision stage, and conference posters and thesis chapters end up with inconsistent figure styles.
Our solution
- Study area maps, multi-panel change maps, thematic and choropleth maps, and 3D terrain views designed to the target journal's figure specifications, through our GIS mapping and cartography services.
- Projection, datum and base layers chosen for the study region, with administrative boundaries used as published.
- Colour-blind-safe and greyscale-safe palettes, consistent styles across a thesis or paper, and exports at the required resolution and format.
- Editable project files handed over so the author can make final changes during review.
- Optional 3D terrain and digital twin visuals for presentations and outreach.
Benefits
- Figures that meet editorial requirements at first submission, reducing revision cycles.
- A consistent visual style across a thesis, its papers and conference posters.
- Maps that communicate results clearly to non-specialist readers, funders and policy users.
Privacy & data security
- Maps showing survey or sampling locations near homes use aggregated or displaced points so that no individual or household can be identified, in line with ICMR's rule against publishing information that may reveal a participant's identity [13].
- Photographs of people are used in figures only where consent for publication has been obtained [13].
- Unpublished results and draft figures are shared only with the named research team.
5. Reproducible Research and FAIR Data
The FAIR Guiding Principles, published in Scientific Data in 2016, set out that research data should be Findable, Accessible, Interoperable and Reusable, with particular emphasis on making data usable by machines as well as people [12]. For geospatial work this means open formats, standard metadata describing coordinate systems, sources and lineage, persistent identifiers, and code that reproduces each result from raw inputs.
National policy points in the same direction. The National Geospatial Policy 2022 encourages open standards, open data and platforms, and best-practice standards for data and technology interoperability [1]. Open-source GIS tools and open formats also mean that a laboratory's work does not depend on a licence that may lapse when a grant ends.
Business problem
Many research groups keep data in personal folders, proprietary project files and undocumented spreadsheets. When a scholar graduates, the next student often cannot reconstruct how a key dataset was produced. Journals and funders increasingly ask for data availability statements and shared code, and assembling these at submission time is slow and error-prone.
Our solution
- An audit of the group's existing spatial data, formats and scripts, followed by a clear folder structure and naming convention.
- Conversion to open formats such as GeoPackage, GeoTIFF and Cloud Optimised GeoTIFF, with metadata recording sources, coordinate systems, processing lineage and licence.
- Versioned Python or R scripts that reproduce figures and tables from raw inputs.
- Packaging for deposit in the institution's or a public data repository, and optional WebGIS viewers to share results with collaborators, funders or the public through our WebGIS services.
Benefits
- Results that examiners, reviewers and future students can reproduce [12].
- Data and code that survive changes in staff, software versions and licences.
- Easier compliance with funder and journal data-sharing expectations.
- Interoperable datasets that can be combined with national open data and other groups' work [1].
Privacy & data security
- Datasets that include personal data are de-identified before sharing: direct identifiers removed, locations aggregated or displaced, and small cells suppressed where they could reveal individuals.
- Access-restricted versions with full detail stay in a controlled store with role-based access and audit logs.
- Data from priced or licensed sources is documented with its licence so that it is not redistributed in breach of terms.
6. Funded Research Projects and Programmes
Externally funded projects are a major channel for geospatial research. ISRO's RESPOND programme, running since the 1970s, provides financial and technical support to universities and academic institutions for research in space science, technology and applications relevant to the national space programme [4]. The Anusandhan National Research Foundation is to receive Rs 50,000 crore during 2023-28 across its funds, including a Rs 14,000 crore budgetary provision from the Central Government [5].
Multi-year projects often involve several institutions, field teams across districts or states, periodic progress reports and final outputs that must be accessible to the funder and other users.
Business problem
Principal investigators juggle teaching, supervision and administration alongside project delivery. Field data from several partner institutions arrives in different formats, progress reports need up-to-date maps, and project-funded staff change over the project's life. Without a shared spatial data system, knowledge and data are lost at each transition.
Our solution
- A shared project data store and WebGIS portal where partner institutions upload and view data in one consistent structure.
- Standardised field forms in the My GLOBEIR app used by all partner teams, with a WebMap dashboard showing progress by site.
- Monitoring dashboards for progress review meetings, built with our monitoring tools.
- Report-ready maps and figures for progress and final reports, and an archive package at project close.
Benefits
- One authoritative project dataset instead of scattered partner files.
- Up-to-date maps for review meetings and reports without last-minute rework.
- Continuity when project staff change, because data and methods are documented.
- Outputs ready for wider use, in line with the national aim of making publicly funded geospatial data accessible for scientific purposes [2].
Privacy & data security
- Access is granted by role and institution, so each partner sees what the project agreement permits.
- Projects can be hosted on GLOBEIR-managed India cloud, the lead institution's own servers or cloud, or MeitY-empanelled government cloud for government-funded clients.
- GLOBEIR follows the institution's information security policies and supports its audits and vendor assessments.
7. GIS Teaching Labs and Capacity Building
The National Geospatial Policy 2022 records that geospatial education is offered in around 200 universities and institutions but lacks a standardised curriculum and is not adequately integrated into the innovation system [1]. It sets a 2030 milestone to enhance capabilities, skills and awareness, plans a Geospatial Skill Council with NSDC to develop NSQF-aligned qualifications, and calls for geospatial education from school to university, centres of excellence such as IIRS, and widely available online courses [1].
National programmes already reach very large numbers. The IIRS Outreach Programme, which began in 2007 with twelve universities, has run 270 live and interactive courses with over 11.9 lakh participants from more than 4,065 academic institutions, government departments and industry, with no course fee [3]. India's higher education system, with 1,168 universities and university-level institutions and 45,473 colleges in 2021-22 [6], creates steady demand for practical, hands-on GIS teaching.
Business problem
Many departments want students to work on real data with real tools, but face outdated lab setups, no curated local datasets, few prepared exercises and limited faculty time to build them. Online lectures build awareness, but students still need guided practice on field data collection, analysis and map-making to become employable or research-ready.
Our solution
- Advice on an open-source GIS and remote sensing lab stack suited to the department's hardware and budget.
- Curated teaching datasets for the institution's own district or state, drawn from Bhuvan, Bhoonidhi and other open sources [7][8].
- Practical lab manuals and exercises covering data download, classification, spatial analysis, field survey with the My GLOBEIR app and cartography.
- Hands-on workshops and faculty development sessions, complementing free national programmes such as the IIRS Outreach Programme [3].
Benefits
- Students practise on familiar local geography rather than generic foreign samples.
- Faculty gain reusable exercises and datasets for every semester.
- Practical skills that align with the national push for standardised geospatial education [1].
- Industry example: the scale of IIRS's free outreach courses shows strong demand for geospatial learning across Indian institutions [3].
Privacy & data security
- Teaching datasets are built from public, non-personal data wherever possible.
- Where student field exercises collect data about people, they follow the institution's ethics guidance and collect only what the exercise needs [13].
- Student accounts on the app are created for coursework only, with account deletion on request within 30 days.