Almost all countries are making increasingly bold commitments to halt and reverse biodiversity loss, minimise the impacts of climate change, and transition to more sustainable development. The effective achievement of many of these commitments relies on integrated spatial planning frameworks that are adaptable to national circumstances, priorities and capabilities. This need is formally recognized by Target 1 of the Kunming-Montreal Global Biodiversity Framework (GBF), which specifies that all areas should be under such planning. Here, we describe the development and application of an operational framework for national-level integrated spatial planning: Essential Life Support Areas (ELSAs). This framework facilitates the identification of areas that - if protected, restored, or sustainably managed - can support the achievement of national commitments to biodiversity, climate, and sustainable development. The process of mapping ELSAs relies heavily on leadership by national experts and stakeholders and the integration of spatial data using systematic conservation planning tools. We showcase the ELSA process carried out for Ecuador, where the use of real-time scenario analyses enabled diverse stakeholder groups to collaborate to assess national priorities for nature, climate, and sustainable development, view trade-offs and synergies, and arrive at a spatial plan to guide national action. ELSA presented an actionable approach for Ecuador, and 12 other pilot countries, to create a spatial plan aimed at fulfilling their national and international commitments to nature, including to the GBF. ### Competing Interest Statement The authors have declared no competing interest.

Recent advances in deep learning, large-scale cloud computing, and open access to decades of Earth observation from public satellite constellations have enabled a breakthrough in automated mapping and monitoring at global scale in near real time. We report on work generating a sequence of annual, global land use and land cover maps at 10 m spatial resolution for years 2017 through 2022, publicly available as an open science product. Each map required processing over 2 million Copernicus Sentinel-2 scenes (approximately 0.6 petabytes). Each map was completed in approximately one week using commercial cloud computing resources. We report our map accuracy and recent work to stabilize the maps across time for monitoring changes across years.

Land use / land cover (LULC) maps provide critical information to governments, land use planners, and decision-makers about the spatial layout of the environment and how it is changing. While a variety of LULC products exist, they are often coarse in resolution, not updated regularly, or require manual editing to be useful. In partnership, Esri, Microsoft Planetary Computer, and Impact Observatory created the world’s first publicly available 10-m LULC map by automating and sharing a deep-learning model that was run on over 450,000 Sentinel-2 scenes. The resulting map, released freely on Esri’s Living Atlas in June 2021, displays ten classes across the globe: built area, trees, scrub/shrub, cropland, bare ground, flooded vegetation, water, grassland, permanent snow/ice, clouds. Here, we discuss key findings from the resulting map, including a quantitative analysis of how 10-m resolution allows us to assess small, low density urban areas compared to other LULC products, including the Copernicus CGLS-LC100 100-m resolution global map. We will also share how we support project-based, on-demand LULC mapping and will present preliminary findings from a new globally consistent 2017-2021 annual LULC dataset across the entire Sentinel-2 archive.

Land use/land cover (LULC) maps are foundational geospatial data products needed by analysts and decision makers across governments, civil society, industry, and finance to monitor global environmental change and measure risk to sustainable livelihoods and development. There is a strong need for high-level, automated geospatial analysis products that turn these pixels into actionable insights for non-geospatial experts. The Sentinel 2 satellites, first launched in mid-2015, are excellent candidates for LULC mapping due to their high spatial, spectral, and temporal resolution. Advances in deep learning and scalable cloud-based compute now provide the analysis capability required to unlock the value in global satellite imagery observations. Based on a novel, very large dataset of over 5 billion human-labeled Sentinel-2 pixels, we developed and deployed a deep learning segmentation model on Sentinel-2 data to create a global LULC map at 10m resolution that achieves state-of-the-art accuracy and enables automated LULC mapping from time series observations.

The recent computing performance revolution has driven improvements in sensor, communication, and storage technology. Multi-decadal remote sensing datasets at the petabyte scale are now available in commercial clouds, with new satellite constellations generating petabytes/year of daily high-resolution global coverage imagery. Cloud computing and storage, combined with recent advances in machine learning, are enabling understanding of the world at a scale and at a level of detail never before feasible. We present results from an ongoing effort to develop satellite imagery analysis tools that aggregate temporal, spatial, and spectral information and that can scale with the high-rate and dimensionality of imagery being collected. We focus on the problem of monitoring food crop productivity across the Middle East and North Africa, and show how an analysis-ready, multi-sensor data platform enables quick prototyping of satellite imagery analysis algorithms, from land use/land cover classification and natural resource mapping, to yearly and monthly vegetative health change trends at the structural field level.

The recent computing performance revolution has driven improvements in sensor, communication, and storage technology. Multi-decadal remote sensing datasets at the petabyte scale are now available in commercial clouds, with new satellite constellations generating petabytes/year of daily high-resolution global coverage imagery. Cloud computing and storage, combined with recent advances in machine learning, are enabling understanding of the world at a scale and at a level of detail never before feasible. We show data processing at terabyte rates in the cloud using multi-modal sensor data and use the calibrated, georeferenced imagery to build videos of the Earth at varying temporal and spatial resolutions. Such temporal-spectral-spatial views of the world enable a range of climate monitoring and change-detection applications. Here we demonstrate one application by using MODIS satellite imagery temporal stacks to classify land cover over North America, and explore the use of synthetic aperture imagery (SAR) to enhance the resulting category mask.

The proliferation of transistors has increased the performance of computing systems by over a factor of a million in the past 30 years, and is also dramatically increasing the amount of data in existence, driving improvements in sensor, communication and storage technology. Multi-decadal Earth and planetary remote sensing global datasets at the petabyte (8×1015 bits) scale are now available in commercial clouds (e.g., Google Earth Engine and Amazon NASA NEX), and new commercial satellite constellations are planning to generate petabytes of images per year, providing daily global coverage at a few meters per pixel. Cloud storage with adjacent high-bandwidth compute, combined with recent advances in machine learning for computer vision, is enabling understanding of the world at a scale and at a level of granularity never before feasible. We report here on a computation processing over a petabyte of compressed raw data from 2.8 quadrillion pixels (2.8 petapixels) acquired by the US Landsat and MODIS programs over the past 40 years. Using commodity cloud computing resources, we convert the imagery to a calibrated, georeferenced, multiresolution tiled format suited for machine-learning analysis. We believe ours is the first application to process, in less than a day, on generally available resources, over a petabyte of scientific image data. We report on work using this reprocessed dataset for experiments demonstrating country-scale food production monitoring, an indicator for famine early warning. We apply remote sensing science and machine learning algorithms to detect and classify agricultural crops and then estimate crop yields.

This report describes recent efforts on our continuous development of a synthetic signal injection method for quantification of metabolite content in MR spectroscopy and MRI. Previous work showed that conversion of spectral peaks to quantitative units of metabolite content could be achieved with a calibrated synthetic free induction decay generated by an inductively coupled injection coil. This work demonstrates that calibrated synthetic voxels, injected in the same manner, can be used to quantify metabolite content in real F-19 image voxels. Images of vials containing different concentrations of sodium fluoride (NaF) were converted to units of moles by reference to precalibrated synthetically injected voxels. Additional images of vials containing variable sodium chloride (NaCI) demonstrate that the quantification process is robust and immune to changes in coil loading conditions. Magn Reson Med 63:570-573, 2010. (C) 2010 Wiley-Liss, Inc.

While it is possible to accurately predict the execution time of a given iteration of an adaptive application, it is not generally possible to predict the data-dependent adaptive behavior the application will take and therefore to predict the total execution time for a given execution. To remedy this situation we have developed an executable performance model that can be utilized dynamically at runtime directly from the application of interest. In this manner, the application itself can rapidly predict the expected execution time for its next iteration based on current information on the data layout and level of adaptivity. This enables the application itself to determine: if an optimum level of performance is being achieved (i.e. by comparing measured and predicted times); when to perform a checkpoint (if the next iteration will exceed a predefined time limit between checkpoints); or when to terminate (if the next iteration will exceed the application's system time allocation for instance). The dynamic model is shown to have high accuracy over a number of test cases, even in the presence of interference (system activities that are not a part of application activities).

The performance of unstructured mesh applications presents a number of complexities and subtleties that do not arise for dense structured meshes. From a programming point of view, the handling of unstructured meshes has an increased complexity in order to manage the necessary data structures and interactions between mesh-cells. From a performance point of view, there are added difficulties in understanding both the processing time on a single processor and the scaling characteristics when using large-scale parallel systems. In this work we present a general performance model for the calculation of deterministic S N transport on unstructured meshes that is also applicable to structured meshes. The model captures the key processing characteristics of the calculation and is parametric using both system performance data (latency, bandwidth, processing rate etc.) and application data (mesh size etc.) as input. A single formulation of the model is used to predict the performance of two quite different implementations of the same calculation. It is validated on two clusters (an HP AlphaServer and an Itanium-2 system) showing high prediction accuracy.

A custom display was built into the MR radiofrequency headcoil to project high-resolution, wide field-of-view stereographic images. Advanced stimulus presentation technologies such as the one described could potentially contribute to a better understanding of the relation between what people are thinking or experiencing, and their associated patterns of brain activity (www.vrpain.com).

In this work we present a predictive analytical model that encompasses the performance and scaling characteristics of a non-deterministic particle transport application, MCNP. Previous studies on the scalability of parallel Monte Carlo eigenvalue calculations have been rather general in nature [[1]]. It can be used for the simulation of neutron, photon, electron, or coupled transport, and has found uses in many problem areas. The performance model is validated against measurements on an AlphaServer ES40 system showing high accuracy across many processor / problem combinations. It is parametric with both application characteristics (e.g. problem size), and system characteristics (e.g. communication latency, bandwidth, achieved processing rate) serving as input. The model is used to provide insight into the achievable performance that should be possible on systems containing thousands of processors and to quantify the impact that possible improvements in sub-system performance may have. In addition, the impact on performance of modifying the communication structure of the code is also quantified.

The key contribution of this paper is the first general model which can be used to predict the running time of transport sweeps on orthogonal grids for any regular mapping of the grid cells to processors. Our model, which accounts for machine dependent parameters such as computation cost and communication latency, can be used to analyze and compare the effects of various spatial decompositions on the running time of the transport sweep. Insight obtained from the model yields two significant contributions to the theory of optimal transport sweeps on orthogonal grids. First, our model provides a theoretical basis which explains why, and under what circumstances, the column decomposition of the current standard KBA algorithm is superior to the 'balanced' decomposition obtained by classic domain decomposition techniques. Second, our model enables us to identify a new decomposition, we call Hybrid , which proves to be almost as good as, and sometimes superior to, the current standard KBA method. Our analysis covers sweeps in two- and three-dimensional spatial domains, and first considers sweeps in only one direction, and then sweeps involving multiple simultaneous directions. We obtain expressions for the completion time and discuss theoretical results.