Benchmarking Individual Tree Mapping with Sub-meter Imagery

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

OPTIMADE, an API for exchanging materials data

: 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

OPTIMADE, an API for exchanging materials data.

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

Dark side of UGC: A user-centric perspective on the impact of user-generated content

User-generated content (UGC) has been on the rise with the emergence of Web 2.0. UGC has led to numerous innovations and has transformed our world in many ways. While the positive impact of UGC is abundant, there is limited research on its negative impact. In this thesis, we study the impact of the UGC from the perspective of the users. This thesis has four main contributions. First, we study the impact of the UGC on the geo-privacy of the user. We investigate the accuracy of localness methods used for the categorization of UGC at the city, county, and state scale through a user study and highlight its impact on users. Second, through a study of political communication on Twitter, we analyze the impact of national flags in UGC. Our results show that flags remain an influential symbol in online communication for most political parties in Germany and the USA. Third, we present a personal password meter for limiting the impact of UGC on the online privacy of the users. Through a user study, we find that our tool significantly limits the inclusion of personal information in passwords, thus limiting the negative impacts of UGC on online security. Finally, we present a deep learning-based approach for identifying individual trees at a large scale, with which we detect over 1.8 billion individual trees in 1.3 million sq. km area in Western Africa. Our assessment suggests a way to monitor trees outside forests globally and to explore their role in mitigating soil degradation, and climate change. While content generation is associated with some adverse impacts on the user, it also offers an opportunity for large scale UGC-based citizen science platforms. In the future, large scale citizen platforms might be crucial for tackling global challenges such as shrinking biodiversity, and the presented approach could be crucial for bootstrapping such platforms

The Role of Flag Emoji in Online Political Communication

Flags are important national symbols that have transcended into the digital world with inclusion in the Unicode character set. Despite their significance, there is little information about their role in online communication. This article examines the role of flag emoji in political communication online by analyzing 640,676 tweets by the most important political parties and Members of Parliament in Germany and the United States. We find that national flags are frequently used in political communication and are mostly used in-line with political ideology. As off-line, flag emoji usage in online communication is associated with external events of national importance. This association is stronger in the United States than in Germany. The results also reveal that the presence of the national flag emoji is associated with significantly higher engagement in Germany irrespective of party, whereas it is associated with slightly higher engagement for politicians of the Republican party and slightly lower engagement for Democrats in the United States. Implications of the results and future research directions are discussed.publishe

Detection and mapping of artillery craters with very high spatial resolution satellite imagery and deep learning

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, primarily in agricultural fields. The availability of such crater maps is extremely valuable within demining and chemical decontamination efforts and can assist in assessing the impact of warfare on agriculture and the environment. We outline the current limitations of the proposed approach and avenues for further research for improving artillery crater detection and mapping

Digital twinning of all forest and non-forest trees at national level via deep learning

Abstract Intelligent forest management is the key to mitigating climate warming, fostering a green economy, and protecting valuable habitats. Detailed knowledge about forests is a prerequisite for such management but is conventionally based on costly plot-scale data, rarely available at resolution of relevance for management strategies. Here, we present a deep learning-based framework that provides location, crown area and height for each individual tree from aerial images at country scale. We quantify and characterize all individual trees in Denmark and show that 26% of the trees grow outside forests, which is typically unrecognized in national inventories. Furthermore, we demonstrate that only marginal effort is needed to transfer our framework to Finland, despite markedly dissimilar landscapes and data sources. Our work lays the foundation for a global database, where every tree has its digital twin and is spatially traceable and manageable