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Landscape restoration initiatives are mainly focusing on forest regeneration and agroforestry, especially in the Global South. However, due to a lack of monitoring tools, the carbon balance of restoration efforts remains poorly quantified. Here, we use satellite images from 2008 and 2019 to calculate carbon stocks for individual trees in Rwanda, a country which has been actively engaged in restoration activities over the past decade. We show that smallholder farmers on average planted about 3 trees per farm during 2008–2019, contributing about 50.4 million new trees at the national scale. The overall C sink of the new farmland trees was 0.13 Megagrams of Carbon per hectare per year, which is 6 times lower than gains observed from restoration of degraded forests (0.76 Megagrams of Carbon per hectare per year). If national greenhouse gas emissions remain at the level of 2019, agroforestry (~61% of national area coverage) and continued restoration of degraded natural forests (~0.5% of national area coverage) have the potential to offset about 80% of the national emissions before 2050. Our work monitors and quantifies progress and impact of landscape restoration projects and outlines a pathway to engage smallholder farmers with a limited number of on-farm trees into the expanding carbon market.

Landscape restoration initiatives are mainly focusing on forest regeneration and agroforestry, especially in the Global South. However, due to a lack of monitoring tools, the carbon balance of restoration efforts remains poorly quantified. Here, we use satellite images from 2008 and 2019 to calculate carbon stocks for individual trees in Rwanda, a country which has been actively engaged in restoration activities over the past decade. We show that smallholder farmers on average planted about 3 trees per farm during 2008–2019, contributing about 50.4 million new trees at the national scale. The overall C sink of the new farmland trees was 0.13 Megagrams of Carbon per hectare per year, which is 6 times lower than gains observed from restoration of degraded forests (0.76 Megagrams of Carbon per hectare per year). If national greenhouse gas emissions remain at the level of 2019, agroforestry (~61% of national area coverage) and continued restoration of degraded natural forests (~0.5% of national area coverage) have the potential to offset about 80% of the national emissions before 2050. Our work monitors and quantifies progress and impact of landscape restoration projects and outlines a pathway to engage smallholder farmers with a limited number of on-farm trees into the expanding carbon market.

The volume of unlabelled Earth observation (EO) data is huge, but many important applications lack labelled training data. However, EO data offers the unique opportunity to pair data from different modalities and sensors automatically based on geographic location and time, at virtually no human labor cost. We seize this opportunity to create a diverse multi-modal pretraining dataset at global scale. Using this new corpus of 1.2 million locations, we propose a Multi-Pretext Masked Autoencoder (MP-MAE) approach to learn general-purpose representations for optical satellite images. Our approach builds on the ConvNeXt V2 architecture, a fully convolutional masked autoencoder (MAE). Drawing upon a suite of multi-modal pretext tasks, we demonstrate that our MP-MAE approach outperforms both MAEs pretrained on ImageNet and MAEs pretrained on domain-specific satellite images. This is shown on several downstream tasks including image classification and semantic segmentation. We find that multi-modal pretraining notably improves the linear probing performance, e.g. 4pp on BigEarthNet and 16pp on So2Sat, compared to pretraining on optical satellite images only. We show that this also leads to better label and parameter efficiency which are crucial aspects in global scale applications., Comment: Data and code is available on the project page: https://vishalned.github.io/mmearth

Unexploded munitions are some of the most enduring remnants of conflicts around the world. Their effects on the economy, health, environment, and post-conflict rehabilitation are long reaching and devastating for the areas they plague. With the advancements in very high spatial resolution (VHR) satellite multispectral imaging at sub-meter resolution, it becomes possible to detect object attributes at the scale of individual impacts (craters) of heavy weapon shelling. Manual identification and delineation of artillery craters in satellite imagery is time and resource consuming, especially when large territories and volumes of VHR data are considered. Therefore, automatic image processing methods should be explored. Here, we evaluate the application of a deep learning approach for identifying and mapping artillery craters in agricultural fields in Eastern Ukraine during the onset of armed conflict in 2014. The model was applied to pansharpened multispectral VHR imagery acquired by the WorldView-2 satellite at 0.5-m spatial resolution. The model can detect artillery craters with producer's accuracy (PA) (or recall) of 0.671 and user's accuracy (UA) (or precision) of 0.392 in terms of crater area and shape, and PA of 0.559 and UA of 0.427 in terms of binary crater identification. The model's performance is dependent on crater size. Reliability of crater detection and mapping improves as the size of craters increases. For example, for craters larger than 60 m2 PA is 0.803 and UA is 0.449 (per-pixel), and PA is 0.891 and UA is 0.721 (per-object). Overall, the model prioritizes PA over UA, i.e., omission error over commission error, and is better at detecting craters than their shapes. We applied the trained model to a separate, 858 km2 subregion of Donetsk oblast to automatically estimate and map the locations, number and area of artillery craters. Our estimates revealed over 22,000 craters in the subregion, which occupy an area of 1.2 km2, or 0.14% of the region, primari

Inland surface water, especially lakes and small water bodies, are essential resources and have impacts on biodiversity, greenhouse gases and health. This is particularly true in the semi-arid Sahelian region, where these resources remain largely unassessed, and little is known about their number, size and quality. Remote sensing monitoring methods remain a promising tool to address these issues at the large scale, especially in areas where field data are scarce. Thanks to technological advances, current remote sensing systems provide data for regular monitoring over time and offer a high spatial resolution, up to 10 metres. Several water detection methods have been developed, many of them using spectral information to differentiate water surfaces from soil, through thresholding on water indices (MNDWI for example), or classifications by clustering. These methods are sensitive to optical reflectance variability and are not straight forwardly applicable to regions, such as the Sahel, where the lakes and their environment are very diverse. Particularly, the presence of aquatic vegetation is an important challenge and source of error for many of the existing algorithms and available databases. Deep learning, a subset of machine learning methods for training deep neural networks, has emerged as the state-of-the-art approach for a large number of remote sensing tasks. In this study, we apply a deep learning model based on the U-Net architecture to detect water bodies in the Sahel using Sentinel-2 MSI data, and 86 manually defined lake polygons as training data. This framework was originally developed for tree mapping (Brandt et al., 2020, https://doi.org/10.1038/s41586-020-2824-5). Our preliminary analysis indicate that our models achieve a good accuracy (98 %). The problems of aquatic vegetation do not appear anymore, and each lake is thus well delimited irrespective of water type and characteristics. Using the water delineations obtained

Unexploded munitions are some of the most enduring remnants of conflicts around the world. Their effects on the economy, health, environment, and post-conflict rehabilitation are long reaching and devastating for the areas they plague. With the advancements in very high spatial resolution (VHR) satellite multispectral imaging at sub-meter resolution, it becomes possible to detect object attributes at the scale of individual impacts (craters) of heavy weapon shelling. Manual identification and delineation of artillery craters in satellite imagery is time and resource consuming, especially when large territories and volumes of VHR data are considered. Therefore, automatic image processing methods should be explored. Here, we evaluate the application of a deep learning approach for identifying and mapping artillery craters in agricultural fields in Eastern Ukraine during the onset of armed conflict in 2014. The model was applied to pansharpened multispectral VHR imagery acquired by the WorldView-2 satellite at 0.5-m spatial resolution. The model can detect artillery craters with producer's accuracy (PA) (or recall) of 0.671 and user's accuracy (UA) (or precision) of 0.392 in terms of crater area and shape, and PA of 0.559 and UA of 0.427 in terms of binary crater identification. The model's performance is dependent on crater size. Reliability of crater detection and mapping improves as the size of craters increases. For example, for craters larger than 60 m2 PA is 0.803 and UA is 0.449 (per-pixel), and PA is 0.891 and UA is 0.721 (per-object). Overall, the model prioritizes PA over UA, i.e., omission error over commission error, and is better at detecting craters than their shapes. We applied the trained model to a separate, 858 km2 subregion of Donetsk oblast to automatically estimate and map the locations, number and area of artillery craters. Our estimates revealed over 22,000 craters in the subregion, which occupy an area of 1.2 km2, or 0.14% of the region, primari

The consistent monitoring of trees both inside and outside of forests is key to sustainable land management. Current monitoring systems either ignore trees outside forests or are too expensive to be applied consistently across countries on a repeated basis. Here we use the PlanetScope nanosatellite constellation, which delivers global very high-resolution daily imagery, to map both forest and non-forest tree cover for continental Africa using images from a single year. Our prototype map of 2019 (RMSE = 9.57%, bias = -6.9%). demonstrates that a precise assessment of all tree-based ecosystems is possible at continental scale, and reveals that 29% of tree cover is found outside areas previously classified as tree cover in state-of-the-art maps, such as in croplands and grassland. Such accurate mapping of tree cover down to the level of individual trees and consistent among countries has the potential to redefine land use impacts in non-forest landscapes, move beyond the need for forest definitions, and build the basis for natural climate solutions and tree-related studies.

Sustainable tree resource management is the key to mitigating climate warming, fostering a green economy, and protecting valuable habitats. Detailed knowledge about tree resources is a prerequisite for such management but is conventionally based on plot-scale data, which often neglects trees outside forests. Here, we present a deep learning-based framework that provides location, crown area, and height for individual overstory trees from aerial images at country scale. We apply the framework on data covering Denmark and show that large trees (stem diameter >10 cm) can be identified with a low bias (12.5%) and that trees outside forests contribute to 30% of the total tree cover, which is typically unrecognized in national inventories. The bias is high (46.6%) when our results are evaluated against all trees taller than 1.3 m, which involve undetectable small or understory trees. Furthermore, we demonstrate that only marginal effort is needed to transfer our framework to data from Finland, despite markedly dissimilar data sources. Our work lays the foundation for digitalized national databases, where large trees are spatially traceable and manageable.

The distribution of dryland trees and their density, cover, size, mass and carbon content are not well known at sub-continental to continental scales 1-14. This information is important for ecological protection, carbon accounting, climate mitigation and restoration efforts of dryland ecosystems 15-18. We assessed more than 9.9 billion trees derived from more than 300,000 satellite images, covering semi-arid sub-Saharan Africa north of the Equator. We attributed wood, foliage and root carbon to every tree in the 0-1,000 mm year -1 rainfall zone by coupling field data 19, machine learning 20-22, satellite data and high-performance computing. Average carbon stocks of individual trees ranged from 0.54 Mg C ha -1 and 63 kg C tree -1 in the arid zone to 3.7 Mg C ha -1 and 98 kg tree -1 in the sub-humid zone. Overall, we estimated the total carbon for our study area to be 0.84 (±19.8%) Pg C. Comparisons with 14 previous TRENDY numerical simulation studies 23 for our area found that the density and carbon stocks of scattered trees have been underestimated by three models and overestimated by 11 models, respectively. This benchmarking can help understand the carbon cycle and address concerns about land degradation 24-29. We make available a linked database of wood mass, foliage mass, root mass and carbon stock of each tree for scientists, policymakers, dryland-restoration practitioners and farmers, who can use it to estimate farmland tree carbon stocks from tablets or laptops.

Inland surface water, especially lakes and small water bodies, are essential resources and have impacts on biodiversity, greenhouse gases and health. This is particularly true in the semi-arid Sahelian region, where these resources remain largely unassessed, and little is known about their number, size and quality. Remote sensing monitoring methods remain a promising tool to address these issues at the large scale, especially in areas where field data are scarce. Thanks to technological advances, current remote sensing systems provide data for regular monitoring over time and offer a high spatial resolution, up to 10 metres. Several water detection methods have been developed, many of them using spectral information to differentiate water surfaces from soil, through thresholding on water indices (MNDWI for example), or classifications by clustering. These methods are sensitive to optical reflectance variability and are not straight forwardly applicable to regions, such as the Sahel, where the lakes and their environment are very diverse. Particularly, the presence of aquatic vegetation is an important challenge and source of error for many of the existing algorithms and available databases. Deep learning, a subset of machine learning methods for training deep neural networks, has emerged as the state-of-the-art approach for a large number of remote sensing tasks. In this study, we apply a deep learning model based on the U-Net architecture to detect water bodies in the Sahel using Sentinel-2 MSI data, and 86 manually defined lake polygons as training data. This framework was originally developed for tree mapping (Brandt et al., 2020, https://doi.org/10.1038/s41586-020-2824-5). Our preliminary analysis indicate that our models achieve a good accuracy (98 %). The problems of aquatic vegetation do not appear anymore, and each lake is thus well delimited irrespective of water type and characteristics. Using the water delineations obtained

Trees sustain livelihoods and mitigate climate change but a predominance of trees outside forests and limited resources make it difficult for many tropical countries to conduct automated nation-wide inventories. Here, we propose an approach to map the carbon stock of each individual overstory tree at the national scale of Rwanda using aerial imagery from 2008 and deep learning. We show that 72% of the mapped trees are located in farmlands and savannas and 17% in plantations, accounting for 48.6% of the national aboveground carbon stocks. Natural forests cover 11% of the total tree count and 51.4% of the national carbon stocks, with an overall carbon stock uncertainty of 16.9%. The mapping of all trees allows partitioning to any landscapes classification and is urgently needed for effective planning and monitoring of restoration activities as well as for optimization of carbon sequestration, biodiversity and economic benefits of trees.

Very high-resolution satellite images and deep learning are achieving the mapping of individual trees and shrubs over large areas in Africa. Each woody plant is precisely georeferenced and defined by its crown area and, sometimes, its height. The challenge is to build allometric equations for foliage, wood and root dry masses based either on crown area alone, or the product crown area × tree height as independent variables regardless of species. This was met by reanalyzing existing Sahel woody plant data from destructive sampling. Overall, the foliage (seasonal maximum), wood and root dry masses were measured on 900, 698 and 26 trees or shrubs from 27, 26 and 5 species respectively. The regression models tested for foliage, wood or root masses were linear or power functions using a range of different approaches: Ordinary least square (OLS) regression after log–log transform with or without Baskerville correction (1972), and non-linear regressions (NLR). All the models outputs were intercompared for fit indicators and prediction uncertainty, as well as the resulting trends in root to wood ratio, and foliage to wood ratio over the range of crown areas. This process selected a set of OLS log–log equations with crown area as independent variable. When the Baskerville correction is applied the approximation errors are 0.4 kg Dry Matter (DM) for foliage, 12.2 for wood and 6.3 for roots, aggregating at 13.8 kg DM per tree. These allometry equations were compared to published equations for tropical trees, most from the more humid tropics that were generally based on stem diameter, tree height and wood density. This paper's allometry predictions were within the range of these other allometry equations, reinforcing the confidence in their use even beyond the Sahel domain towards sub-humid savannas.

The consistent monitoring of trees both inside and outside of forests is key to sustainable land management. Current monitoring systems either ignore trees outside forests or are too expensive to be applied consistently across countries on a repeated basis. Here we use the PlanetScope nanosatellite constellation, which delivers global very high-resolution daily imagery, to map both forest and non-forest tree cover for continental Africa using images from a single year. Our prototype map of 2019 (RMSE = 9.57%, bias = -6.9%). demonstrates that a precise assessment of all tree-based ecosystems is possible at continental scale, and reveals that 29% of tree cover is found outside areas previously classified as tree cover in state-of-the-art maps, such as in croplands and grassland. Such accurate mapping of tree cover down to the level of individual trees and consistent among countries has the potential to redefine land use impacts in non-forest landscapes, move beyond the need for forest definitions, and build the basis for natural climate solutions and tree-related studies.

The distribution of dryland trees and their density, cover, size, mass and carbon content are not well known at sub-continental to continental scales 1-14. This information is important for ecological protection, carbon accounting, climate mitigation and restoration efforts of dryland ecosystems 15-18. We assessed more than 9.9 billion trees derived from more than 300,000 satellite images, covering semi-arid sub-Saharan Africa north of the Equator. We attributed wood, foliage and root carbon to every tree in the 0-1,000 mm year -1 rainfall zone by coupling field data 19, machine learning 20-22, satellite data and high-performance computing. Average carbon stocks of individual trees ranged from 0.54 Mg C ha -1 and 63 kg C tree -1 in the arid zone to 3.7 Mg C ha -1 and 98 kg tree -1 in the sub-humid zone. Overall, we estimated the total carbon for our study area to be 0.84 (±19.8%) Pg C. Comparisons with 14 previous TRENDY numerical simulation studies 23 for our area found that the density and carbon stocks of scattered trees have been underestimated by three models and overestimated by 11 models, respectively. This benchmarking can help understand the carbon cycle and address concerns about land degradation 24-29. We make available a linked database of wood mass, foliage mass, root mass and carbon stock of each tree for scientists, policymakers, dryland-restoration practitioners and farmers, who can use it to estimate farmland tree carbon stocks from tablets or laptops.

The consistent monitoring of trees both inside and outside of forests is key to sustainable land management. Current monitoring systems either ignore trees outside forests or are too expensive to be applied consistently across countries on a repeated basis. Here we use the PlanetScope nanosatellite constellation, which delivers global very high-resolution daily imagery, to map both forest and non-forest tree cover for continental Africa using images from a single year. Our prototype map of 2019 (RMSE = 9.57%, bias = −6.9%). demonstrates that a precise assessment of all tree-based ecosystems is possible at continental scale, and reveals that 29% of tree cover is found outside areas previously classified as tree cover in state-of-the-art maps, such as in croplands and grassland. Such accurate mapping of tree cover down to the level of individual trees and consistent among countries has the potential to redefine land use impacts in non-forest landscapes, move beyond the need for forest definitions, and build the basis for natural climate solutions and tree-related studies.

Trees sustain livelihoods and mitigate climate change but a predominance of trees outside forests and limited resources make it difficult for many tropical countries to conduct automated nation-wide inventories. Here, we propose an approach to map the carbon stock of each individual overstory tree at the national scale of Rwanda using aerial imagery from 2008 and deep learning. We show that 72% of the mapped trees are located in farmlands and savannas and 17% in plantations, accounting for 48.6% of the national aboveground carbon stocks. Natural forests cover 11% of the total tree count and 51.4% of the national carbon stocks, with an overall carbon stock uncertainty of 16.9%. The mapping of all trees allows partitioning to any landscapes classification and is urgently needed for effective planning and monitoring of restoration activities as well as for optimization of carbon sequestration, biodiversity and economic benefits of trees.

Trees inside cities are important for the urban microclimate, contributing positively to the physical and mental health of the urban dwellers. Despite their importance, often only limited information about city trees is available. Therefore in this paper, we propose a method for mapping urban trees in high-resolution aerial imagery using limited datasets and deep learning. Deep learning has become best-practice for this task, however, existing approaches rely on large and accurately labelled training datasets, which can be difficult and expensive to obtain. However, often noisy and incomplete data may be available that can be combined and utilized to solve more difficult tasks than those datasets were intended for.This paper studies how to combine accurate point labels of urban trees along streets with crowd-sourced annotations from an open geographic database to delineate city trees in remote sensing images, a task which is challenging even for humans. To that end, we perform semantic segmentation of very high resolution aerial imagery using a fully convolutional neural network.The main challenge is that our segmentation maps are sparsely annotated and incomplete. Small areas around the point labels of the street trees coming from official and crowd-sourced data are marked as foreground class. Crowd-sourced annotations of streets, buildings, etc. define the background class. Since the tree data is incomplete, we introduce a masking to avoid class confusion.Our experiments in Hamburg, Germany, showed that the system is able to produce tree cover maps, not limited to trees along streets, without providing tree delineations. We evaluated the method on manually labelled trees and show that performance drastically deteriorates if the open geographic database is not used.

Trees inside cities are important for the urban microclimate, contributing positively to the physical and mental health of the urban dwellers. Despite their importance, often only limited information about city trees is available. Therefore in this paper, we propose a method for mapping urban trees in high-resolution aerial imagery using limited datasets and deep learning. Deep learning has become best-practice for this task, however, existing approaches rely on large and accurately labelled training datasets, which can be difficult and expensive to obtain. However, often noisy and incomplete data may be available that can be combined and utilized to solve more difficult tasks than those datasets were intended for. This paper studies how to combine accurate point labels of urban trees along streets with crowd-sourced annotations from an open geographic database to delineate city trees in remote sensing images, a task which is challenging even for humans. To that end, we perform semantic segmentation of very high resolution aerial imagery using a fully convolutional neural network. The main challenge is that our segmentation maps are sparsely annotated and incomplete. Small areas around the point labels of the street trees coming from official and crowd-sourced data are marked as foreground class. Crowd-sourced annotations of streets, buildings, etc. define the background class. Since the tree data is incomplete, we introduce a masking to avoid class confusion. Our experiments in Hamburg, Germany, showed that the system is able to produce tree cover maps, not limited to trees along streets, without providing tree delineations. We evaluated the method on manually labelled trees and show that performance drastically deteriorates if the open geographic database is not used.

There is a rising interest in mapping trees using satellite or aerial imagery, but there is no standardized evaluation protocol for comparing and enhancing methods. In dense canopy areas, the high variability of tree sizes and their spatial proximity makes it arduous to define the quality of the predictions. Concurrently, object-centric approaches such as bounding box detection usuallyperform poorly on small and dense objects. It thus remains unclear what is the ideal framework for individual tree mapping, in regards to detection and segmentation approaches, convolutional neural networks and transformers. In this paper, we introduce an evaluation framework suited for individual tree mapping in any physical environment, with annotation costs and applicative goals in mind. We review and compare different approaches and deep architectures, and introduce a new method that we experimentally prove to be a good compromise between segmentation and detection.

As-the-crow-flies (ATCF) navigation is an alternative to turn-by-turn navigation for cyclists utilizing the least-angle strategy by providing the beeline to the destination. However, past research found weaknesses (e.g., running into dead ends) affecting the user experience. In this paper, we investigate how the street network attributes to the experience of the navigation method. Using a feature importance analysis and comparison of different city types across 1633 cities, we analyze how ATCF navigation fits different environments. The perfect ACTF-city has long streets, many options to turn at decision points, few dead ends, and a grid-like structure. Cities well suited are primarily found in East Asia and North America. Furthermore, we find that previous ATCF studies were most likely conducted in Western Europe, which features the least suited street networks for the navigation method. We present design implications for future ATCF implementations and argue for diverse study locations in future research., As-the-crow-flies (ATCF) navigation is an alternative to turn-by-turn navigation for cyclists utilizing the least-angle strategy by providing the beeline to the destination. However, past research found weaknesses (e.g., running into dead ends) affecting the user experience. In this paper, we investigate how the street network attributes to the experience of the navigation method. Using a feature importance analysis and comparison of different city types across 1633 cities, we analyze how ATCF navigation fits different environments. The perfect ACTF-city has long streets, many options to turn at decision points, few dead ends, and a grid-like structure. Cities well suited are primarily found in East Asia and North America. Furthermore, we find that previous ATCF studies were most likely conducted in Western Europe, which features the least suited street networks for the navigation method. We present design implications for future ATCF implementations and argue for diverse study locations in future research.

Deep learning methods hold great promise for the automatic analysis of large-scale remote sensing data in archaeological research. Here, we present a robust approach to locating ancient Maya architectures (buildings, aguadas, and platforms) based on integrated segmentation of satellite imagery and aerial laser scanning data. Deep learning models with different architectures and loss functions were trained and combined to form an ensemble for pixel-wise classification. We applied both training data augmentation as well as test-time augmentation and performed morphological cleaning in the postprocessing phase. Our approach was evaluated in the context of the “Discover the mysteries of the Maya: An Integrated Image Segmentation Challenge” at ECML PKDD 2021 and achieved one of the best results with an average IoU of 0.8183.

As-the-crow-flies (ATCF) navigation is an alternative to turn-by-turn navigation for cyclists utilizing the least-angle strategy by providing the beeline to the destination. However, past research found weaknesses (e.g., running into dead ends) affecting the user experience. In this paper, we investigate how the street network attributes to the experience of the navigation method. Using a feature importance analysis and comparison of different city types across 1633 cities, we analyze how ATCF navigation fits different environments. The perfect ACTF-city has long streets, many options to turn at decision points, few dead ends, and a grid-like structure. Cities well suited are primarily found in East Asia and North America. Furthermore, we find that previous ATCF studies were most likely conducted in Western Europe, which features the least suited street networks for the navigation method. We present design implications for future ATCF implementations and argue for diverse study locations in future research., As-the-crow-flies (ATCF) navigation is an alternative to turn-by-turn navigation for cyclists utilizing the least-angle strategy by providing the beeline to the destination. However, past research found weaknesses (e.g., running into dead ends) affecting the user experience. In this paper, we investigate how the street network attributes to the experience of the navigation method. Using a feature importance analysis and comparison of different city types across 1633 cities, we analyze how ATCF navigation fits different environments. The perfect ACTF-city has long streets, many options to turn at decision points, few dead ends, and a grid-like structure. Cities well suited are primarily found in East Asia and North America. Furthermore, we find that previous ATCF studies were most likely conducted in Western Europe, which features the least suited street networks for the navigation method. We present design implications for future ATCF implementations and argue for diverse study locations in future research.

Deep learning methods hold great promise for the automatic analysis of large-scale remote sensing data in archaeological research. Here, we present a robust approach to locating ancient Maya architectures (buildings, aguadas, and platforms) based on integrated segmentation of satellite imagery and aerial laser scanning data. Deep learning models with different architectures and loss functions were trained and combined to form an ensemble for pixel-wise classification. We applied both training data augmentation as well as test-time augmentation and performed morphological cleaning in the postprocessing phase. Our approach was evaluated in the context of the “Discover the mysteries of the Maya: An Integrated Image Segmentation Challenge” at ECML PKDD 2021 and achieved one of the best results with an average IoU of 0.8183.

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification.

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification., Funding Agencies|MARVEL National Centre of Competence in Research - Swiss National Science Foundation [51NF40-182892]; European Centre of Excellence MaX "Materials design at the Exascale" [824143]; "MARKETPLACE" H2020 project [760173]; Swedish e-Science Research Centre (SeRC); Swedish Research Council (VR)Swedish Research Council [2016-04810, 2020-05402]; European Unions Horizon 2020 Research and Innovation Program [654360 NFFA-EUROPE, 689868]; Royal SocietyRoyal Society of LondonEuropean Commission; U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering DivisionUnited States Department of Energy (DOE) [DE-AC02-05-CH11231]; European Unions Horizon 2020 research and innovation program under the European Unions Grant agreement [951786]; EPSRC CDT in Computational Methods for Materials Science [EP/L015552/1]; Toyota Research Institute; U.S. Department of Commerce, National Institute of Standards and Technology, Center for Hierarchical Materials Design (CHiMaD) [70NANB14H012]; MICCoM, as part of the Computational Materials Sciences Program - U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering DivisionUnited States Department of Energy (DOE); EPSRCUK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC) [EP/M022595/1, EP/T026642/1]; EPSRC High-End Computing Consorium, UKCPUK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC) [EP/P022561/1]; F.R.S.-FNRSFonds de la Recherche Scientifique - FNRS; European Unions Horizon 2020 research and innovation program under the European Unions Grant [676580]; Max Planck research network on big-data-driven materials science (BiGmax); DOD-ONROffice of Naval ResearchUnited States Department of Defense [N00014-17-1-2090, N00014-17-1-2876]; National Science Foundation under DMREF GrantNational Science Foundation (NSF)NSF - Directorate for Computer &

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification.

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification.

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification., Funding Agencies|MARVEL National Centre of Competence in Research - Swiss National Science Foundation [51NF40-182892]; European Centre of Excellence MaX "Materials design at the Exascale" [824143]; "MARKETPLACE" H2020 project [760173]; Swedish e-Science Research Centre (SeRC); Swedish Research Council (VR)Swedish Research Council [2016-04810, 2020-05402]; European Unions Horizon 2020 Research and Innovation Program [654360 NFFA-EUROPE, 689868]; Royal SocietyRoyal Society of LondonEuropean Commission; U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering DivisionUnited States Department of Energy (DOE) [DE-AC02-05-CH11231]; European Unions Horizon 2020 research and innovation program under the European Unions Grant agreement [951786]; EPSRC CDT in Computational Methods for Materials Science [EP/L015552/1]; Toyota Research Institute; U.S. Department of Commerce, National Institute of Standards and Technology, Center for Hierarchical Materials Design (CHiMaD) [70NANB14H012]; MICCoM, as part of the Computational Materials Sciences Program - U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering DivisionUnited States Department of Energy (DOE); EPSRCUK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC) [EP/M022595/1, EP/T026642/1]; EPSRC High-End Computing Consorium, UKCPUK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC) [EP/P022561/1]; F.R.S.-FNRSFonds de la Recherche Scientifique - FNRS; European Unions Horizon 2020 research and innovation program under the European Unions Grant [676580]; Max Planck research network on big-data-driven materials science (BiGmax); DOD-ONROffice of Naval ResearchUnited States Department of Defense [N00014-17-1-2090, N00014-17-1-2876]; National Science Foundation under DMREF GrantNational Science Foundation (NSF)NSF - Directorate for Computer &

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification., Funding Agencies|MARVEL National Centre of Competence in Research - Swiss National Science Foundation [51NF40-182892]; European Centre of Excellence MaX "Materials design at the Exascale" [824143]; "MARKETPLACE" H2020 project [760173]; Swedish e-Science Research Centre (SeRC); Swedish Research Council (VR)Swedish Research Council [2016-04810, 2020-05402]; European Unions Horizon 2020 Research and Innovation Program [654360 NFFA-EUROPE, 689868]; Royal SocietyRoyal Society of LondonEuropean Commission; U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering DivisionUnited States Department of Energy (DOE) [DE-AC02-05-CH11231]; European Unions Horizon 2020 research and innovation program under the European Unions Grant agreement [951786]; EPSRC CDT in Computational Methods for Materials Science [EP/L015552/1]; Toyota Research Institute; U.S. Department of Commerce, National Institute of Standards and Technology, Center for Hierarchical Materials Design (CHiMaD) [70NANB14H012]; MICCoM, as part of the Computational Materials Sciences Program - U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering DivisionUnited States Department of Energy (DOE); EPSRCUK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC) [EP/M022595/1, EP/T026642/1]; EPSRC High-End Computing Consorium, UKCPUK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC) [EP/P022561/1]; F.R.S.-FNRSFonds de la Recherche Scientifique - FNRS; European Unions Horizon 2020 research and innovation program under the European Unions Grant [676580]; Max Planck research network on big-data-driven materials science (BiGmax); DOD-ONROffice of Naval ResearchUnited States Department of Defense [N00014-17-1-2090, N00014-17-1-2876]; National Science Foundation under DMREF GrantNational Science Foundation (NSF)NSF - Directorate for Computer &

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification., Funding Agencies|MARVEL National Centre of Competence in Research - Swiss National Science Foundation [51NF40-182892]; European Centre of Excellence MaX "Materials design at the Exascale" [824143]; "MARKETPLACE" H2020 project [760173]; Swedish e-Science Research Centre (SeRC); Swedish Research Council (VR)Swedish Research Council [2016-04810, 2020-05402]; European Unions Horizon 2020 Research and Innovation Program [654360 NFFA-EUROPE, 689868]; Royal SocietyRoyal Society of LondonEuropean Commission; U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering DivisionUnited States Department of Energy (DOE) [DE-AC02-05-CH11231]; European Unions Horizon 2020 research and innovation program under the European Unions Grant agreement [951786]; EPSRC CDT in Computational Methods for Materials Science [EP/L015552/1]; Toyota Research Institute; U.S. Department of Commerce, National Institute of Standards and Technology, Center for Hierarchical Materials Design (CHiMaD) [70NANB14H012]; MICCoM, as part of the Computational Materials Sciences Program - U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering DivisionUnited States Department of Energy (DOE); EPSRCUK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC) [EP/M022595/1, EP/T026642/1]; EPSRC High-End Computing Consorium, UKCPUK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC) [EP/P022561/1]; F.R.S.-FNRSFonds de la Recherche Scientifique - FNRS; European Unions Horizon 2020 research and innovation program under the European Unions Grant [676580]; Max Planck research network on big-data-driven materials science (BiGmax); DOD-ONROffice of Naval ResearchUnited States Department of Defense [N00014-17-1-2090, N00014-17-1-2876]; National Science Foundation under DMREF GrantNational Science Foundation (NSF)NSF - Directorate for Computer &

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification., Funding Agencies|MARVEL National Centre of Competence in Research - Swiss National Science Foundation [51NF40-182892]; European Centre of Excellence MaX "Materials design at the Exascale" [824143]; "MARKETPLACE" H2020 project [760173]; Swedish e-Science Research Centre (SeRC); Swedish Research Council (VR)Swedish Research Council [2016-04810, 2020-05402]; European Unions Horizon 2020 Research and Innovation Program [654360 NFFA-EUROPE, 689868]; Royal SocietyRoyal Society of LondonEuropean Commission; U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering DivisionUnited States Department of Energy (DOE) [DE-AC02-05-CH11231]; European Unions Horizon 2020 research and innovation program under the European Unions Grant agreement [951786]; EPSRC CDT in Computational Methods for Materials Science [EP/L015552/1]; Toyota Research Institute; U.S. Department of Commerce, National Institute of Standards and Technology, Center for Hierarchical Materials Design (CHiMaD) [70NANB14H012]; MICCoM, as part of the Computational Materials Sciences Program - U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering DivisionUnited States Department of Energy (DOE); EPSRCUK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC) [EP/M022595/1, EP/T026642/1]; EPSRC High-End Computing Consorium, UKCPUK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC) [EP/P022561/1]; F.R.S.-FNRSFonds de la Recherche Scientifique - FNRS; European Unions Horizon 2020 research and innovation program under the European Unions Grant [676580]; Max Planck research network on big-data-driven materials science (BiGmax); DOD-ONROffice of Naval ResearchUnited States Department of Defense [N00014-17-1-2090, N00014-17-1-2876]; National Science Foundation under DMREF GrantNational Science Foundation (NSF)NSF - Directorate for Computer &

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification.

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification., Funding Agencies|MARVEL National Centre of Competence in Research - Swiss National Science Foundation [51NF40-182892]; European Centre of Excellence MaX "Materials design at the Exascale" [824143]; "MARKETPLACE" H2020 project [760173]; Swedish e-Science Research Centre (SeRC); Swedish Research Council (VR)Swedish Research Council [2016-04810, 2020-05402]; European Unions Horizon 2020 Research and Innovation Program [654360 NFFA-EUROPE, 689868]; Royal SocietyRoyal Society of LondonEuropean Commission; U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering DivisionUnited States Department of Energy (DOE) [DE-AC02-05-CH11231]; European Unions Horizon 2020 research and innovation program under the European Unions Grant agreement [951786]; EPSRC CDT in Computational Methods for Materials Science [EP/L015552/1]; Toyota Research Institute; U.S. Department of Commerce, National Institute of Standards and Technology, Center for Hierarchical Materials Design (CHiMaD) [70NANB14H012]; MICCoM, as part of the Computational Materials Sciences Program - U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering DivisionUnited States Department of Energy (DOE); EPSRCUK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC) [EP/M022595/1, EP/T026642/1]; EPSRC High-End Computing Consorium, UKCPUK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC) [EP/P022561/1]; F.R.S.-FNRSFonds de la Recherche Scientifique - FNRS; European Unions Horizon 2020 research and innovation program under the European Unions Grant [676580]; Max Planck research network on big-data-driven materials science (BiGmax); DOD-ONROffice of Naval ResearchUnited States Department of Defense [N00014-17-1-2090, N00014-17-1-2876]; National Science Foundation under DMREF GrantNational Science Foundation (NSF)NSF - Directorate for Computer &

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification.

A significant amount of our daily lives is dedicated to driving, leading to an unavoidable exposure to driving-related stress. The rise of autonomous vehicles will likely lessen the extent of this stress and enhance the routine traveling experience. Yet, no matter how diverse they may be, current routing criteria are limited to considering only the passive preferences of a vehicle's users. Thus, to enhance the overall driving experience in autonomous vehicles, we advocate here for the diversification of routing criteria, by additionally emphasizing activity- and mood-based requirements., Comment: 5 pages, 1 figure

A major challenge in training deep learning models is the lack of high quality and complete datasets. In the paper, we present a masking approach for training deep learning models from a publicly available but incomplete dataset. For example, city of Hamburg, Germany maintains a list of trees along the roads, but this dataset does not contain any information about trees in private homes and parks. To train a deep learning model on such a dataset, we mask the street trees and aerial images with the road network. Road network used for creating the mask is downloaded from OpenStreetMap, and it marks the area where the training data is available. The mask is passed to the model as one of the inputs and it also coats the output. Our model learns to successfully predict trees only in the masked region with 78.4% accuracy., Comment: 5 pages, 3 figures

A large proportion of dryland trees and shrubs (hereafter referred to collectively as trees) grow in isolation, without canopy closure. These non-forest trees have a crucial role in biodiversity, and provide ecosystem services such as carbon storage, food resources and shelter for humans and animals1,2. However, most public interest relating to trees is devoted to forests, and trees outside of forests are not well-documented3. Here we map the crown size of each tree more than 3 m2 in size over a land area that spans 1.3 million km2 in the West African Sahara, Sahel and sub-humid zone, using submetre-resolution satellite imagery and deep learning4. We detected over 1.8 billion individual trees (13.4 trees per hectare), with a median crown size of 12 m2, along a rainfall gradient from 0 to 1,000 mm per year. The canopy cover increases from 0.1% (0.7 trees per hectare) in hyper-arid areas, through 1.6% (9.9 trees per hectare) in arid and 5.6% (30.1 trees per hectare) in semi-arid zones, to 13.3% (47 trees per hectare) in sub-humid areas. Although the overall canopy cover is low, the relatively high density of isolated trees challenges prevailing narratives about dryland desertification5–7, and even the desert shows a surprisingly high tree density. Our assessment suggests a way to monitor trees outside of forests globally, and to explore their role in mitigating degradation, climate change and poverty.