Monitoring SDG 9 with global open data and open software : a case study from rural Tanzania

The 17 goals adopted by the United Nations (UN) are aimed at achieving a better and more sustainable future for all. For each goal, a set of indicators has been defined. The indicators measure progress towards achieving the respective SDG. For the majority of these indicators, geospatial information is needed to evaluate the current state of the indicator. While geospatial information is largely available in developed countries, this is not the case in many developing countries of the world. Furthermore, skills and capacity for calculating indicator values are also limited in many developing countries. To address these shortcomings, the third challenge of the 2018 UN OSGeo Committee Educational Challenges called for the development of training material for using open source software together with freely available high resolution global geospatial datasets in support of monitoring SDG progress. The resulting training material provides a step-by-step guide for calculating the state of SDG indicator 9.1.1, Proportion of the rural population who live within 2km of an all-season road, using open software and open data with global coverage. Through the development of this training material, we showed that anyone can monitor progress towards achieving SDG indicator 9.1.1 for their specific part of the world. Because open source software and open data were used, the indicator calculation is cost effective and completely sustainable.https://www.isprs.org/publications/archives.aspxpm2020Geography, Geoinformatics and Meteorolog

A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat

A spatiotemporal machine learning framework for automated prediction and analysis of long-term Land Use/Land Cover dynamics is presented. The framework includes: (1) harmonization and preprocessing of spatial and spatiotemporal input datasets (GLAD Landsat, NPP/VIIRS) including five million harmonized LUCAS and CORINE Land Cover-derived training samples, (2) model building based on spatial k-fold cross-validation and hyper-parameter optimization, (3) prediction of the most probable class, class probabilities and model variance of predicted probabilities per pixel, (4) LULC change analysis on time-series of produced maps. The spatiotemporal ensemble model consists of a random forest, gradient boosted tree classifier, and an artificial neural network, with a logistic regressor as meta-learner. The results show that the most important variables for mapping LULC in Europe are: seasonal aggregates of Landsat green and near-infrared bands, multiple Landsat-derived spectral indices, long-term surface water probability, and elevation. Spatial cross-validation of the model indicates consistent performance across multiple years with overall accuracy (a weighted F1-score) of 0.49, 0.63, and 0.83 when predicting 43 (level-3), 14 (level-2), and five classes (level-1). Additional experiments show that spatiotemporal models generalize better to unknown years, outperforming single-year models on known-year classification by 2.7% and unknown-year classification by 3.5%. Results of the accuracy assessment using 48,365 independent test samples shows 87% match with the validation points. Results of time-series analysis (time-series of LULC probabilities and NDVI images) suggest forest loss in large parts of Sweden, the Alps, and Scotland. Positive and negative trends in NDVI in general match the land degradation and land restoration classes, with “urbanization” showing the most negative NDVI trend. An advantage of using spatiotemporal ML is that the fitted model can be used to predict LULC in years that were not included in its training dataset, allowing generalization to past and future periods, e.g. to predict LULC for years prior to 2000 and beyond 2020. The generated LULC time-series data stack (ODSE-LULC), including the training points, is publicly available via the ODSE Viewer. Functions used to prepare data and run modeling are available via the eumap library for Python

Current trends in the management of groundwater specific geospatial information

The purpose of this paper is to present the state-of-art of groundwater geospatial information management, highlighting the relevant data model characteristics and technical implementation of the European Directive 2007/2/EC, also known as the INSPIRE Directive. The maturity of the groundwater geodata management systems is of crucial importance for any kind of activity, be it a research project or an operational service of monitoring, protection or exploitation activities. An ineffective and inadequate geodata management system can significantly increase costs or even overthrow the entire activity ([1-3]). Furthermore, following the technological advancement and the extended scientific and operational interdisciplinary connectivity at national and international scale, the interoperability characteristics are becoming increasingly important in the development of groundwater geospatial information management. From paper recordings to digital spreadsheets, from relational database to standardized data models, the manner in which the groundwater data was gathered, stored, processed and visualized has changed significantly over time. Aside from the clear technical progress, the design that captures the natural connections and dependencies between each groundwater feature and phenomena have also evolved. The second part of our paper address the variations that occurred when outlining the different groundwater geospatial information management models, differences that depict the complexity of hydrogeological data

Designing a cartographic online application in order to assess the Danube Delta evolution

The Danube Delta, one of the most important wetlands in Europe, went through many transformations during the last century, mainly due to human interference. When analyzing the current state of this ecosystem, one must consider the historical changes that have a significant influence on the evolution of the delta. Written documents are, no doubt, valuable, but a fundamental contribution is given by the study of geospatial information (old maps, satellite images etc). These information depict the ecosystems components state at a specific time and analyzing them in a successive manner allows one to extract information, that otherwise would be difficult to perceive, such as patterns of evolution. Another important aspect that could be resolved through the use of historical maps is the evolution of human settlements and the toponymy