Background

The North Eastern Region (NER) is witnessing a gradual rise in age-related cognitive disorders such as dementia and memory loss among the elderly population. Many families in remote and rural areas face challenges in accessing specialized neurological care, cognitive therapy, and long-term elderly support services due to limited healthcare infrastructure and geographical barriers. Elderly patients suffering from dementia often experience memory decline, confusion, anxiety, and social isolation, while caregivers face difficulties in continuous monitoring and engagement. There is limited availability of affordable and culturally inclusive digital therapeutic solutions tailored for elderly individuals in the North-Eastern Region. To strengthen elderly healthcare and improve cognitive well-being, there is a need for an accessible, engaging, and Al-enabled cognitive gaming solution designed specifically for dementia patients in NER.

Description

This problem statement seeks the development of an AI-powered cognitive gaming and memory assistance platform for elderly dementia patients in the North Eastern Region. The solution should

Expected Solution

A user-friendly Al-enabled cognitive assistance platform with: Adaptive gaming and memory training modules Voice-enabled multilingual interface Cognitive performance tracking and analytics dashboard Caregiver monitoring and alert system Offline synchronization support for remote areas Secure patient data management system Simple and accessible UI/UX designed for elderly users The solution should support early cognitive intervention, improve quality of life for elderly dementia patients, and strengthen digital healthcare accessibility across the North Eastern Region.

Background

With rapid urbanization and vertical growth of cities, conventional 2D land record Systems are becoming inadequate for managing modern urban properties.Existing land administration systems are primarily designed to identify surface-level land parcels and are unable to uniquely define ownership rights associated with multi- storey apartments, underground infrastructure, elevated transport corridors, parking spaces, air-rights, and subsurface utility networks.

Description

The proposed solution Should develop an advanced 3D ULPIN(Unique Land Parcel ldentification Number) Generation and vertical Property Mapping system capable of creating unique spatial identities for:

Expected Solution

The expected outcome is development of a scalable and interoperable 3D cadastral framework capable of:

Background

The All India Institute of Ayurveda (AIIA) conducts and coordinates a growing portfolio of clinical research in Ayurveda — interventional and observational studies, multi-centre trials — and, as the host of the National Pharmacovigilance Coordination Centre (NPvCC) for ASU&H drugs, it also anchors nationwide safety surveillance. This activity is governed by a demanding compliance framework mandatory prospective registration in the Clinical Trials Registry – India (CTRI); the Good Clinical Practice guidelines for ASU medicine (GCP-ASU) and the ICMR National Ethical Guidelines; the New Drugs and Clinical Trials Rules, 2019 where applicable; Institutional Ethics Committee oversight; and timely Adverse-Event / Serious-Adverse-Event reporting. Yet study status, recruitment, milestones, data quality and safety signals are typically tracked across spreadsheets and disconnected tools, with no single, real-time, auditable view. The result is delayed decisions, missed reporting timelines and avoidable compliance risk — precisely as Ayurveda research scales and seeks global scientific credibility.

Description

The platform is a real-time, cloud-based Clinical Trial Management System (CTMS) and monitoring dashboard that gives AIIA a single, role-based, auditable view of its entire clinical-research portfolio.It tracks each study across its lifecycle — protocol and Institutional Ethics Committee approval, CTRI registration, site activation, screening, enrolment and randomization against target, visit and protocol-deviation compliance, data-query and data-quality status, study milestones and timelines, and close-out — surfaced as real-time Key Performance Indicators (KPIs) with configurable alerts (for example, enrolment lag, an ethics approval or CTRI update due, or an overdue monitoring visit).Because AIIA hosts the NPvCC, the dashboard integrates pharmacovigilance: it captures and routes Adverse Drug Reaction / Adverse-Event / Serious-Adverse-Event reports against regulatory reporting timelines, coded to standard dictionaries (MedDRA, WHODrug), and feeds aggregate safety signals to the Data Safety Monitoring Board and institutional leadership.Data must follow recognised clinical-research standards — CDISC (CDASH for data collection, SDTM for tabulation, ADaM for analysis) and HL7 FHIR R4 for interoperability with Electronic Data Capture (EDC), the hospital information system and Ayushman Bharat Digital Mission (ABDM) building blocks — with full ALCOA+ data integrity and an immutable, time-stamped audit trail. Access is strictly role-based (Principal Investigator, study coordinator, monitor, Ethics Committee, pharmacovigilance, administration, and read-only regulator views).The platform must comply with GCP-ASU, the ICMR ethical guidelines, the NDCT Rules 2019 and CTRI requirements, and with the Digital Personal Data Protection Act, 2023 and its 2025 Rules — including informed-consent management, data minimisation, encryption, and hosting on compliant, data-resident cloud infrastructure secured to ISO/IEC 27001 and CERT-In norms, since clinical-trial data is sensitive personal data.

Expected Solution

A deployable, cloud-based CTMS-and-analytics dashboard providing: a real-time portfolio view with per-study drill-down; configurable KPIs and alerting; strictly role-based access and an immutable, ALCOA+-compliant audit trail; CDISC-aligned data models and HL7 FHIR R4 / ABDM interoperability with EDC and the hospital information system; an integrated pharmacovigilance module (AE/SAE capture, MedDRA/WHO Drug coding, regulatory-timeline tracking) reflecting AIIA’s NPvCC role; CTRI and ethics/regulatory milestone tracking; informed-consent and privacy controls aligned to the DPDP regime; electronic-signature and data-integrity controls consistent with GCP; and the ability to export submission-ready datasets (SDTM / ADaM, Define-XML). It should present tailored dashboards for Investigators, the Ethics Committee, pharmacovigilance and institutional leadership, and be hosted on secure, data-resident cloud infrastructure (ISO/IEC 27001, CERT-In). The build can be staged — a core study-tracking and KPI MVP first, then EDC/FHIR integration and the pharmacovigilance module, then full CDISC submission export and advanced analytics. The system should be evaluable on data accuracy and integrity, timeliness of safety and regulatory reporting, interoperability conformance, and access-control and audit completeness.

Develop a comprehensive outreach portal that archives expedition reports, scientific datasets, publications, photographs, videos and institutional activities while generating content for websites and social media.

Background

Subsurface ocean temperature is a fundamental variable for understanding ocean circulation,upper-ocean heat content, stratification, climate variability, air-sea interaction and marine ecosystems. Accurate representation of the vertical ocean temperature is essential for applications such as marine heatwave monitoring, fisheries, and data assimilation, etc. However, direct measurements of subsurface temperature remain sparse because they rely primarily on in-situ observing systems such as ARGO profiling floats, moored buoys, gliders, and ship observations. While these observations provide valuable vertical information, their spatial and temporal coverage is insufficient for generating continuous, basin-scale subsurface fields.In contrast, satellite observations provide continuous, large-scale monitoring of surface ocean conditions at relatively high spatial and temporal resolution. Surface variables such as Sea Surface Temperature (SST), Sea Surface Salinity (SSS), Sea Surface Height (SSH)/Sea Level Anomaly(SLA), surface currents, and surface winds contain indirect signatures of subsurface ocean processes through physical mechanisms including thermocline displacement, mesoscale eddies,vertical mixing, transport, and ocean-atmosphere coupling.Recent advances in Artificial Intelligence (AI), Deep Learning (DL) , and representation learning enable the generation of satellite embeddings, where multidimensional surface observations are transformed into compact latent representations that capture hidden ocean dynamics. Such embeddings offer the potential to learn nonlinear relationships between surface observations and subsurface ocean structure more effectively than conventional machine learning approaches. Detailed

Description

The current problem statement proposes the development of a Satellite Embedding-Based Deep Learning Framework to reconstruct depth-wise subsurface temperature from daily surface satellite observations at 0. 25° spatial resolution for North Indian Ocean (5°N to 30°N and 45°E to 105°E).The objective is to estimate the three-dimensional ocean temperature using only surface satellite observations. The proposed system shall

Expected Solution

End-to-end preprocessing pipeline for satellite and ocean datasets. Satellite embedding engine capable of learning latent ocean representations from surface observations. Deep learning reconstruction model for estimating subsurface temperature. Standardized output at daily temporal resolution and 0. 25° spatial resolution. Validation framework using independent ARGO observations. Demonstration of a working Proof-of-Concept (PoC) over the Bay of Bengal / Arabian Sea

Background

Marine oil spills inflict great damage on marine ecosystems and several times remains un-attributable to the vessel causing such spills. Leveraging satellite imagery along with AIS data will enable detection of oil spills and vessel responsible for the same.

Description

The core challenge attempts to facilitate detection of oil spills and also in identifying the polluting vessel using remote sensing satellite data, such as SAR and EO imagery and AIS data. Participants are to design an intelligent automated pipeline to do the following: (a) Detect and characterise the oil spill and calculating geometric properties and age if feasible. (b) Using oceanographic and meteorological data, it is envisaged to trace the slick towards the origin point and time, predict the future flow of the slick, and (c) analyse and attribute the spill to a vessel using historic AIS data to reconstruct vessel traffic around the origin window in space and time. The irrelevant traffic is to be filtered out and potential suspect vessels are to be scored considering various aspects such as proximity, trajectory, behavioural anomalies etc.

Expected Solution

An automated detection and hindcasting machine learning model that identified oils slicks from satellite imagery, mapping their drift paths backward and forward. It also ranks potential culprit vessel based on spatio-temporal correlation with AIS data. A suitable visual interface is also to be developed.

Background

Image Registration is the process of aligning two or more images of the same scene taken at different times, from different viewpoints, or by different sensors into a common coordinate system. It has two main components Source Image (Moving): The image that is to be geometrically transformed to align with the reference image. Reference Image (Fixed): The target image about which source image is to be geometrically transformed.

Description

The process of lunar images registration involves finding match points between source and reference image and then aligning the source image with the reference image. The key challenges involved in this process are as follows Illumination variation Illumination variation refers to changes in sun azimuth and elevation effect on the surface lighting conditions that affect the appearance of the lunar surface features which is hard to correlate. Viewpoint variation It refers to geometric distortions caused by different camera positions/orientations capturing the same scene. Objects appear shifted, scaled, rotated, or perspective-distorted depending on observing angle. Scale Variation Lunar imaging missions operate at vastly different altitudes and at different spatial resolutions. This creates scale ratios.

Expected Solution

Generic software solution for finding correspondence between Chandrayaan-2 acquired optical images and Lunar reference images with a sub-pixel accuracy of source image maintaining uniform distribution across the images. Software and registered product with corresponding match points. Evaluation metric (eg. RMSE, inlier match count, inlier ratio, etc.)

Background

Remote-sensing imagery is widely used for agricultural monitoring, disaster management, urban planning, forest monitoring, water-resource assessment, infrastructure mapping, and environmental analysis. However, most existing remote-sensing AI solutions are developed as isolated applications for a single predefined task, such as land-cover classification, object detection, visual question answering, or change detection. These systems often require users to understand satellite-data characteristics, GIS workflows, model selection, and task-specific parameters. Consequently, non-expert users may find it difficult to obtain meaningful information from satellite imagery through simple natural-language queries. Many operational remote-sensing questions cannot always be answered reliably using a single optical image. Relevant information may be distributed across paired or multiple observations acquired at different times or by different sensors. Optical and multispectral imagery provides spectral and contextual information, whereas synthetic aperture radar (SAR) provides complementary structural information and supports day-and-night acquisition through cloud cover. Multitemporal image pairs are required to identify and interpret changes over time, while co-registered optical–SAR pairs can provide more complete and reliable information than either modality alone. A general-purpose large language model (LLM) or vision-language model (VLM) cannot be expected to perform these specialised tasks reliably without adaptation to remote-sensing imagery, sensor characteristics, and domain-specific terminology. The proposed solution must therefore include remote-sensing fine-tuning or domain adaptation and may employ multiple specialised models for different tasks. BigEarthNet.txt will serve as the primary dataset for adapting image–text representations to multisensor remote-sensing data. VRSBench and RSVQA will be used to evaluate single-image captioning, grounding, and visual question answering, while CDVQA will be used to evaluate multitemporal change-based visual question answering. The novelty of SatQuery AI lies in its agentic, query-driven framework. Instead of applying a single generic VLM, the system selects and executes suitable remote-sensing specialist models, validates inputs, combines their outputs, and returns an evidence-grounded response.

Description

The objective is to develop SatQuery AI, a software-based agentic vision-language assistant for analysing single and paired remote-sensing images through natural-language queries. Single-image understanding is a mandatory baseline, while the principal focus is joint reasoning over paired cross-modal and multitemporal imagery. Defined Input Scope Single image One optical/multispectral or SAR image for captioning, visual question answering, and text-guided region grounding. Cross-modal pair Co-registered optical/multispectral and SAR images of the same geographic area for joint information extraction and cross-modal analysis. Bi-temporal pair Two spatially corresponding images of the same geographic area acquired at different times for change detection, change description, and change-based visual question answering. Supported formats GeoTIFF or TIFF for geospatial imagery. PNG and JPEG inputs may be accepted only for the prescribed public benchmark datasets. Mandatory Functional Scope Remote-sensing adaptation At least one visual or vision-language component must be fine-tuned or otherwise adapted using BigEarthNet.txt or the any open source training data. Single-image baseline Visual question answering shall be mandatory. Each solution must additionally implement either captioning/scene description or text-guided region grounding. Multi-image change analysis Change description or change-based visual question answering from a bi-temporal image pair shall be mandatory. A spatial change map may also be generated where reference masks are available. Cross-modal pair analysis The system must extract complementary information from a co-registered optical/multispectral and SAR image pair. Agentic orchestration The system must automatically select, sequence, and execute the appropriate specialist models or tools according to the query and input configuration. Representative Queries 'Describe the land-cover and major objects visible in this image.' 'Highlight the water body referred to in the query.' 'What changed between these two dates, and where did the change occur?' 'Use the optical and SAR images together to identify built-up and water-covered regions.' 'Has the built-up area increased, decreased, or remained unchanged?' Agentic Model and Tool Orchestration The system may use multiple specialised components, such as a remote-sensing VQA or captioning model, a grounding model, a change-understanding or change-VQA model, and an optical–SAR fusion or information-extraction model. interpret the query and classify the requested task; check the number, modality, format, metadata, and compatibility of the input images; select one or more models or tools from a predefined registry; configure only permitted task parameters and execute the selected workflow; combine textual and spatial outputs, estimate confidence, and return visual evidence; and provide an auditable execution summary containing the selected task, model/tool names, and key parameters. The controller may perform internal task planning; however, only the observable execution trace, including the selected task, models or tools, permitted parameters, and outputs will be evaluated. Internal reasoning text is neither required nor evaluated.

Expected Solution

The expected solution is an interactive GUI or web application with an agentic remote-sensing AI backend. It should accept supported image inputs and natural-language queries, select the appropriate specialist workflow, and return evidence-grounded textual and visual results. The solution should include Input upload and compatibility checking. A remote-sensing-adapted vision-language component. Specialist tools for VQA, captioning or grounding, change understanding, and optical–SAR analysis. An agentic controller for task routing, tool execution, and output integration. Visual evidence, confidence information, execution summaries, and downloadable reports. Each solution must demonstrate single-image VQA, one additional single-image task, multitemporal change understanding, optical–SAR paired-image analysis, and agentic model/tool orchestration. A generic LLM or VLM without remote-sensing adaptation will not satisfy the requirements.

Deliverables

An interactive GUI or web application with an agentic remote-sensing AI backend, Codes and models including test and demonstration. Implementation Scope The system shall support single optical/multispectral or SAR images, co-registered optical–SAR pairs, and bi-temporal pairs in GeoTIFF/TIFF or approved benchmark formats. It must perform single-image VQA, one additional single-image task, change analysis, optical–SAR joint analysis, and agentic model/tool selection through an interactive GUI or web application. Evaluation/Judging Criteria Final evaluation will use prescribed public benchmark test subsets and an ISRO/SAC evaluation dataset. Scores will be normalised before combining different metrics. Public benchmarks will be evaluated using the prescribed test splits. The ISRO/SAC evaluation set will contain pre-georeferenced and co-registered Cartosat-2S optical and RISAT SAR image pairs, with task-specific reference answers, labels, bounding boxes, or masks, as applicable. Evaluation annotations will not be disclosed to participating teams.

Space technology refers to the application of engineering principles to the design, development, manufacture, and operation of devices and systems for space travel and exploration.