Keynote Lectures

Abstract
Research in the environmental sector is growing in importance over time due to its strong relation to human wellbeing. Ecosystems supply provisioning goods, fulfil regulation and maintenance functions and deliver cultural services. All these benefits are crucial for human wellbeing and for the sustainable development of societies. EO plays an important role in their assessment, because it can be used for quantitative evaluation. Developments take place at an accelerating pace at global scale supported by the launching of international and European initiatives and programmes (e.g. Aichi Targets, Convention on Biological Diversity - CBD indicators, GEO BON, GEO ECO Initiative, ECO FUN, Copernicus Services, Mapping and Assessment of Ecosystems and their Services - MAES, European Nature Information System - EUNIS, Sustainable Development Goals - SDGs, H2020 relevant projects – e.g. E-SHAPE, ECOPOTENTIAL). New monitoring methodologies are now available that combine approaches in geo- and biosciences, remotely sensed data and in-situ observations. Satellite missions, such as the European Sentinels, provide a large amount of high-quality primary and secondary derived data useful for monitoring the environment and ecosystems. In-situ data are being organized and made available through international activities, such as the International Long-Term Ecological Research (ILTER) network and the Critical Zone Exploration Network (CZEN). Ecosystem models capable of assimilating the information from EOs are being developed. Recent technological advances, among others the Open Data Cube technologies and the ECOPOTENTIAL EODESM online tool, deliver unique capabilities to track changes in unprecedented detail using EO data, enabling effective responses to problems of national and international significance and considered supportive to ecosystem status assessments. Let us convene and discuss together about aforementioned, exchange experiences and knowledge during the 6th GISTAM event in Prague.

Abstract
There is a growing demand for data that facilitate highly accurate understanding of the spatiotemporal distribution of both moving and static occupants in urban areas. Currently, a large amount of population data are available, however none of the data provide an accurate understanding of the numbers and departure/arrival locations of moving people using detailed units of space and time. Also, their detailed attributes such as age, sex, and occupation, are not provided either. In this keynote lecture, we would like to introduce some methods to address aforementioned issues, and illustrate some numerical examples.
First, after evaluating the strength and weakness of existing population statistics, which are available in Japan, we propose a method based on maximum likelihood method is investigated for using their strengths to best advantage and compensating for weaknesses. The proposed method is then validated by comparing with another flow data, which featured spatiotemporal data including departure/arrival locations, and demonstrate that the present procedure provides accurate estimates for population flows. This method makes it possible to analyse urban regions from new and never-before employed points of view by identifying the number of transient occupants and their travel directions at any time on high level of detail.
Next, we construct a model that provides spatiotemporal distribution of occupants in urban areas that vary according to clock time, location, and building use classification. The time, location, and building use classification are employed as keys to integrate demographic information. Weekday and weekend data for the central wards of Tokyo are employed to create estimates of the number of occupants with their detailed attributes. Using numerical examples, we demonstrate that the proposed model can provide demographic spatiotemporal distributions with far higher value than before; in which the buildings people occupy, their reasons for being there, their sex and age bracket, and their residential locations, can all be identified.
Finally, we will demonstrate the application examples of integrated demographic data. When a large earthquake occurs, many people are presumed to have difficulty in returning home. However, no research has been achieved yet to discuss the congestion of supporting facilities for stranded people in terms of site, the number and spatial distribution. To address this problem, we construct a simulation model, which describes people’s behavior such as returning home or going to other facilities after an earthquake occurs. Using the model, we estimate the congestion of facilities which varies according to day of the week or the time when the event occurs, and demonstrate the effective methods for reducing the congestion, which include offering information for people and cooperation of private institutions.

Abstract
Maps, “images showing the Earth surface“, have been in existence for several thousand years. Nowadays, every country has its own set of topographic and thematic maps. All maps belong to the national and international wealth of individual countries and regions. Land cover maps show the distribution of the physical and biological cover of the Earth surface in predefined land cover classes. There are examples when the land nomenclature is combined with the land use nomenclature. According to the Food and Agriculture Organization (FAO), land use is a collection of adjustments, activities and inputs that people make in a certain type of land cover. The land cover/land use (LC/LU) topic has been a matter of research for more than 40 years. It has a complicated history which consists of data compiled by many experts, in different ways and using various methods, and for a variety of purposes. Therefore, the LC/LU data differ in various characteristics – geographic coverage, time resolution, scale, processing, data sources, etc. Remote sensing data - results of the Earth observation – formed, in fact, a basis of LC/LU maps together with in-situ data, and still has been playing the main role in data source. The present world has been increasingly analysed in larger areas with a raising amount of applications of LC/LU data which exceed sizes of individual countries and bring new applications. The amount of users of LU/LC data is growing and therefore there are new requirements for such data. It is mainly the variety of land cover/land use classification systems that limits the compatibility and comparability of existing land use/land cover data. It makes analysis of such data – already ready to use - a very difficult task. The presentation is focused on an overview of global, regional and national land cover/land use data from various views which we have collected for a running project named Geoharmonizer: EU-wide automated mapping system for harmonization of Open Data based on FOSS4G and Machine Learning (No. 2018-EU-IA-0095). The second part shows results of a part of the project dedicated to the harmonisation process of two existing European land cover/land use classification systems and its problems.