Global Map

Abstract: . A new version of a digital global map of irrigation areas was developed by combining irrigation statistics for 10 825 sub-national statistical units and geo-spatial information on the location and extent of irrigation schemes. The map shows the percentage of each 5 arc minute by 5 arc minute cell that was equipped for irrigation around the year 2000. It is thus an important data set for global studies related to water and land use. This paper describes the data set and the mapping methodology and gives, for the first time, an estimate of the map quality at the scale of countries, world regions and the globe. Two indicators of map quality were developed for this purpose, and the map was compared to irrigated areas as derived from two remote sensing based global land cover inventories.

Abstract: Global knowledge about the spatial distribution of species is orders of magnitude coarser in resolution than other geographically-structured environmental datasets such as topography or land cover. Yet such knowledge is crucial in deciphering ecological and evolutionary processes and in managing global change. In this review, we propose a conceptual and cyber-infrastructure framework for refining species distributional knowledge that is novel in its ability to mobilize and integrate diverse types of data such that their collective strengths overcome individual weaknesses. The ultimate aim is a public, online, quality-vetted 'Map of Life' that for every species integrates and visualizes available distributional knowledge, while also facilitating user feedback and dynamic biodiversity analyses. First milestones toward such an infrastructure have now been implemented.

Abstract: In this paper, we investigate safe and efficient map-building strategies for a mobile robot with imperfect control and sensing. In the implementation, a robot equipped with a range sensor builds apolygonal map layout of a previously unknown indoor environment. The robot explores the environment and builds the map concurrently by patching together the local models acquired by the sensor into a global map. A well-studied and related problem is the simultaneous localization and mapping SLAM problem, where the goal is to integrate the information collected during navigation into the most accurate map possible. However, SLAM does not address the sensor-placement portion of the map-building task. That is, given the map built so far, where should the robot go next? This is the main question addressed in this paper. Concretely, an algorithm is proposed to guide the robot through a series of ?good? positions, where ?good? refers to the expected amount and quality of the information that will be revealed at each new location. This is similar to the next-best-view NBV problem studied in computer vision and graphics. However, in mobile robotics the problem is complicated by several issues, two of which are particularly crucial. One is to achieve safe navigation despite an incomplete knowledge of the environment and sensor limitations e.g., in range and incidence. The other issue is the need to ensure sufficient overlap between each new local model and the current map, in order to allow registration of successive views under positioning uncertainties inherent to mobile robots. To address both issues in a coherent framework, in this paper we introduce the concept of a safe region, defined as the largest region that is guaranteed to be free of obstacles given the sensor readings made so far. The construction of a safe region takes sensor limitations into account. In this paper we also describe an NBV algorithm that uses the safe-region concept to select the next robot position at each step. The new position is chosen within the safe region in order to maximize the expected gain of information under the constraint that the local model at this new position must have a minimal overlap with the current global map. In the future, NBV and SLAM algorithms should reinforce each other. While a SLAM algorithm builds a map by making the best use of the available sensory data, an NBV algorithm, such as that proposed here, guides the navigation of the robot through positions selected to provide the best sensory inputs.

Abstract: A global map of surface heat flow is presented on a 2° by 2° equal area grid. It is based on a global heat flow data set of over 38,000 measurements. The map consists of three components. Firstly, in regions of young ocean crust (<67.7Ma) the model estimate uses a half-space conduction model based on the age of the oceanic crust, since it is well known that raw data measurements are frequently influenced by significant hydrothermal circulation. Secondly in other regions of data coverage the estimate is based on data measurements. At the map resolution these two categories (young ocean, data covered) cover 65% of Earth’s surface. Thirdly, for all other regions the estimate is based on the assumption that there is a correlation between heat-flow and geology. This assumption is assessed and the correlation is found to provide a minor improvement over assuming that heat flow would be represented by the global average. The map is made available digitally.