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Accurate anticipation of the maize harvest date is important in the agricultural market, as it ensures the sustainability of food production in response to the increasing global demand for food. This paper proposes a predictive model to determine the optimal harvest time in maize plots using the Normalised Difference Vegetation Index (NDVI) and climatological data. These variables were oversampled and used to train various models, including Random Forest (RF), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting Machine (XGBoost), CatBoost and Support Vector Machine (SVM). Bayesian optimisation has been used to find the best hyperparameters and Shapley values to identify the variables that exert the most significant influence on the prediction in each model instance. As a result of this approach, a model with an accuracy of 92.1% and an Area Under the Curve (AUC) of 0.935 was developed. The variables that determined these results were atmospheric pressure, mean temperature, precipitation, NDVI, and precipitation. [ABSTRACT FROM AUTHOR]

In the current world, it is easy to listen that everybody and everything is connected. Over this connected world, the concept of location-based services has grown in order to provide digital services in everyplace and at every time. Nevertheless, this is not 100% true because the connection is not guaranteed for many people and in many places. These are the Degraded Communications Environments (DCE), environments where the availability of high-speed communications is not guaranteed in at least the 75% of the time. This paper works over the experience of a previous work in developing light protocols that do not need broadband for communication. This work provides an extension of these protocols for the inclusion of mobile devices as elements of the communication process and a set of libraries to allow the development of applications in DCE. The work done has involved the development of two frameworks: an Android framework that makes the incorporation of Android devices easier and a server-based framework that provides the server side for the development of the referred applications. A use case that uses these two frameworks has been developed. Finally, all technology developed is available throw a public Git repository. [ABSTRACT FROM AUTHOR]

Geospatial data catalogs enable users to discover and access geographical information. Prevailing solutions are document oriented and fragment the spatial continuum of the geospatial data into independent and disconnected resources described through metadata. Due to this, the complete answer for a query may be scattered across multiple resources, making its discovery and access more difficult. This paper proposes an improved information retrieval process for geospatial data catalogs that aggregates the search results by identifying the implicit spatial/thematic relations between the metadata records of the resources. These aggregations are constructed in such a way that they match better the user query than each resource individually. [ABSTRACT FROM PUBLISHER]

The breakthrough developments in geospatial technologies and the increasing availability of spatial data make geoinformation a business and a decisional element to the management. Hence, it is important to have a management plan to factor in practical and feasible data sources, in building geo applications. The authors of this paper are motivated by the fact that right data sources could outclass in-house resources in various application scenarios. This paper outlines pragmatic cases for the tangible benefits of the existing potential data and expeditious patterns for digital earth. This work also proposes ‘good-enough’ solutions based on the pragmatic cases, available literature, and the 3D city model developed that could be sufficient in contriving the objectives of the common public usage and open business models. To demonstrate this approach, the paper encapsulated the low-cost development of virtual 3D city model using publicly available cadastral data and web services. [ABSTRACT FROM PUBLISHER]

Jurisdictional domains are generally accepted political divisions of the earth surface that cover specific territorial and functional scopes over time. They are frequently used in information retrieval (IR) to classify and locate resources by means of their geographical location. However, the changes they suffer over time reduce their applicability in historical collections. In this context, and with the objective of improving the use of jurisdictional domains, this article proposes an ontology schema that combines in a single model the administrative structure, the spatial components, and the temporal evolution of jurisdictional domains. This ontology schema has been used to create the Spanish jurisdictional model. Additionally, as an example of its applicability in the IR context, the Spanish model has been used as part of a location-based query component for the Spanish Official State Gazette. [ABSTRACT FROM AUTHOR]