This research paper introduces a Federated Learning (FL) framework tailored for multimodal data privacy in disaster analysis, seamlessly integrating geospatial data privacy concerns, privacy preservation techniques, and disaster management applications. The framework adeptly addresses the ethical challenges posed by the usage of geospatial data and the delicate balance between open research and individual privacy. The proposed framework takes advantage of FL's decentralized nature to enable secure and privacy-preserving analysis of multimodal data, including textual content from social media and geospatial information from satellites. Employing advanced privacy preservation techniques, including anonymization and encryption, the framework ensures the protection of individual privacy rights while enhancing disaster management applications [6]. The intersection of these components is pivotal, emphasizing the careful equilibrium between data utility and privacy protection. Furthermore, the paper presents compelling results derived from the application of the proposed FL framework, showcasing its effectiveness in disaster analysis. The results highlight the successful integration of decentralized multimodal data, preserving privacy while contributing valuable insights to enhance disaster management strategies. This work underscores the importance of striking a thoughtful balance between data utility and individual privacy in advancing disaster research and response capabilities [7].

This research introduces an innovative methodology to enhance spatiotemporal data harmonization in soil analytics by leveraging SciBERT [1], an advanced pre-trained large language model specialized in scientific text comprehension. The approach capitalizes on SciBERT's detailed contextual understanding to extract essential scientific concepts from accompanying textual descriptions in spatiotemporal datasets, addressing challenges stemming from limited annotated data. The proposed standardized representation of spatial and temporal dimensions, achieved through semantic enrichment and entity recognition, leads to significant improvements in predicting, aligning, and integrating spatiotemporal information. The methodology involves systematic data gathering on diverse soil types in India, including nutrient content and physical properties, creating a comprehensive fertilizer database through web scraping, preprocessing, and employing NLTK for lemmatization and tokenization. The finetuned SciBERT model is currently employed for manual querying processes, with future plans to integrate this into semantic enrichment. This research explores the application of semantic enrichment using SciBERT to address challenges in soil analytics, for improving spatiotemporal harmonization in soil data.

Biomass burning contributes to large quantities of gaseous pollutants and aerosol particles in the atmosphere, having a significant impact on air quality, human health, and climate. This study examines the complex topic of estimating carbon emissions from grassland biomass burning in the Sahyadri region (Western Ghats). We calculate total burnt area over the period of ~5 months (from January to May 2022) using normalized burned index and innovative methods of subsequent subtraction technique. The burnt area is used to calculate the total carbon dioxide emitted because of burning using IPCC emission factors. Our conservative and liberal estimates indicate 9.9 and 25.83 million tons of carbon dioxide emissions by biomass burning.

Spatio-temporal data harmonization is crucial for effectively managing soil health in sustainable agriculture. In the case of Indian soil, known for its diversity and complexity, integrating and analyzing different soil data sources presents significant challenges. This research proposes a novel approach that utilizes knowledge graphs to address these challenges in soil health management.This paper presents a knowledge graph that captures domain-specific information about Indian soil, such as soil properties, climatic conditions, land use patterns, and agricultural practices. This knowledge graph serves as a unified framework for integrating diverse data sources, such as soil survey lab reports, sensor data, and agricultural databases. An architecture for spatiotemporal data harmonization is presented in this paper, which includes data acquisition, pre-processing, knowledge graph construction, embedding techniques, alignment, harmonization, and integration. In the context of soil health management, it facilitates the integration and analysis of diverse soil data sources.

Geospatial technology holds immense potential for transforming the overall business and educational sector in India. Recently, a National Geospatial Policy was framed and adapted in India. Although, policy claims to foster plethora of opportunities. However, there are few challenges hindering the effective implementation of geospatial policy in various business sector as well as in education field. This paper presents overview of National Geospatial Policy of India with various provisions to upboost business, research, and education in India. Also, the impact, opportunities, and challenges for adaptation of this policy with respect to education are addressed.

Interoperability is a key concern in geospatial domain. Approaches based on Geo-ontology which represents the semantics of the geospatial information are currently popular. However, there still remains the problem of reconciliation of different geo-ontological representations. Hence, many geo-ontology matching systems have been developed to harmonize and integrate various information sources for resolving the heterogeneity and achieve interoperability. However, deriving the degree of interoperability between two information sources is a challenge. Measuring the degree of interoperability helps in understanding, the readiness or the amenability to communicate across similar representations, thus identifying those information sources (out of many similar ones) that can be integrated. This paper describes a Geo-ontology matching framework to measure the degree of interoperability.

Recently an emerging area that focuses on mining data across domains is Cross Domain Similarity Mining and analogical reasoning for data analysis. Geospatial domain is highly interdisciplinary with methods that incorporate multiple other domains; this research focuses on analogy reasoning using geo-ontology matching. Here a framework is proposed for analogy reasoning. Using an example of road networks, it is explained how analogy can be formulated and transferable and learnable patterns are generated.

Geospatial information is different than conventional information. Harmonization is needed for interoperability and seamless access to data. Ontology matching is an emerging solution to achieve this harmonization. The input data of the Geospatial ontologies vary from the conventional ontologies and hence it is conceptualized in a different manner. There are two major obstacles for geoinformation fusion: heterogeneity and uncertainty. Heterogeneity is more prevalent and uncertainty is an unavoidable entity in geospatial domain. This paper explores a novel multi-objective algorithm for geospatial ontology matching. It uses Pareto ranking to sort the probable solution and derives the pareto front. This pareto front is used further to find the best match.

Geographic data is often collected from independent sources and is usually heterogeneous in nature. Integration of these heterogeneous data sources is a crucial task. The recent developments in the semantic web domain have shown great potential to address the geospatial data integration issues. Ontology matching is seen as a solution for integration problems and has attracted wide attention. Geospatial domain is characterized by vagueness moreover semantic ambiguity leads to uncertainty in developing ontology and this is propagated to the ontology-matching phase. Hence, to resolve the uncertainty issues this study focuses on the adaptation of information theory based approaches for geospatial ontology matching.