@article{MuellerLeppichGeissetal.2023,
  author    = {M{\"u}ller, Konstantin and Leppich, Robert and Geiß, Christian and Borst, Vanessa and Pelizari, Patrick Aravena and Kounev, Samuel and Taubenb{\"o}ck, Hannes},
  title     = {Deep neural network regression for normalized digital surface model generation with Sentinel-2 imagery},
  series = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  volume    = {16},
  journal   = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  issn      = {1939-1404},
  doi       = {10.1109/JSTARS.2023.3297710},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-349424},
  pages     = {8508-8519},
  year      = {2023},
  abstract  = {In recent history, normalized digital surface models (nDSMs) have been constantly gaining importance as a means to solve large-scale geographic problems. High-resolution surface models are precious, as they can provide detailed information for a specific area. However, measurements with a high resolution are time consuming and costly. Only a few approaches exist to create high-resolution nDSMs for extensive areas. This article explores approaches to extract high-resolution nDSMs from low-resolution Sentinel-2 data, allowing us to derive large-scale models. We thereby utilize the advantages of Sentinel 2 being open access, having global coverage, and providing steady updates through a high repetition rate. Several deep learning models are trained to overcome the gap in producing high-resolution surface maps from low-resolution input data. With U-Net as a base architecture, we extend the capabilities of our model by integrating tailored multiscale encoders with differently sized kernels in the convolution as well as conformed self-attention inside the skip connection gates. Using pixelwise regression, our U-Net base models can achieve a mean height error of approximately 2 m. Moreover, through our enhancements to the model architecture, we reduce the model error by more than 7\%.},
  language  = {en}
}
@article{WeigandWurmDechetal.2019,
  author    = {Weigand, Matthias and Wurm, Michael and Dech, Stefan and Taubenb{\"o}ck, Hannes},
  title     = {Remote sensing in environmental justice research—a review},
  series = {ISPRS International Journal of Geo-Information},
  volume    = {8},
  journal   = {ISPRS International Journal of Geo-Information},
  number    = {1},
  issn      = {2220-9964},
  doi       = {10.3390/ijgi8010020},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-196950},
  year      = {2019},
  abstract  = {Human health is known to be affected by the physical environment. Various environmental influences have been identified to benefit or challenge people's physical condition. Their heterogeneous distribution in space results in unequal burdens depending on the place of living. In addition, since societal groups tend to also show patterns of segregation, this leads to unequal exposures depending on social status. In this context, environmental justice research examines how certain social groups are more affected by such exposures. Yet, analyses of this per se spatial phenomenon are oftentimes criticized for using "essentially aspatial" data or methods which neglect local spatial patterns by aggregating environmental conditions over large areas. Recent technological and methodological developments in satellite remote sensing have proven to provide highly detailed information on environmental conditions. This narrative review therefore discusses known influences of the urban environment on human health and presents spatial data and applications for analyzing these influences. Furthermore, it is discussed how geographic data are used in general and in the interdisciplinary research field of environmental justice in particular. These considerations include the modifiable areal unit problem and ecological fallacy. In this review we argue that modern earth observation data can represent an important data source for research on environmental justice and health. Especially due to their high level of spatial detail and the provided large-area coverage, they allow for spatially continuous description of environmental characteristics. As a future perspective, ongoing earth observation missions, as well as processing architectures, ensure data availability and applicability of 'big earth data' for future environmental justice analyses.},
  language  = {en}
}
@article{DongWurmTaubenboeck2022,
  author    = {Dong, Ruirui and Wurm, Michael and Taubenb{\"o}ck, Hannes},
  title     = {Seasonal and diurnal variation of land surface temperature distribution and its relation to land use/land cover patterns},
  series = {International Journal of Environmental Research and Public Health},
  volume    = {19},
  journal   = {International Journal of Environmental Research and Public Health},
  number    = {19},
  issn      = {1660-4601},
  doi       = {10.3390/ijerph191912738},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-290393},
  year      = {2022},
  abstract  = {The surface urban heat island (SUHI) affects the quality of urban life. Because varying urban structures have varying impacts on SUHI, it is crucial to understand the impact of land use/land cover characteristics for improving the quality of life in cities and urban health. Satellite-based data on land surface temperatures (LST) and derived land use/cover pattern (LUCP) indicators provide an efficient opportunity to derive the required data at a large scale. This study explores the seasonal and diurnal variation of spatial associations from LUCP and LST employing Pearson correlation and ordinary least squares regression analysis. Specifically, Landsat-8 images were utilized to derive LSTs in four seasons, taking Berlin as a case study. The results indicate that: (1) in terms of land cover, hot spots are mainly distributed over transportation, commercial and industrial land in the daytime, while wetlands were identified as hot spots during nighttime; (2) from the land composition indicators, the normalized difference built-up index (NDBI) showed the strongest influence in summer, while the normalized difference vegetation index (NDVI) exhibited the biggest impact in winter; (3) from urban morphological parameters, the building density showed an especially significant positive association with LST and the strongest effect during daytime.},
  language  = {en}
}
@article{WurmStarkZhuetal.2019,
  author    = {Wurm, Michael and Stark, Thomas and Zhu, Xiao Xiang and Weigand, Matthias and Taubenb{\"o}ck, Hannes},
  title     = {Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks},
  series = {ISPRS Journal of Photogrammetry and Remote Sensing},
  volume    = {150},
  journal   = {ISPRS Journal of Photogrammetry and Remote Sensing},
  doi       = {10.1016/j.isprsjprs.2019.02.006},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-233799},
  pages     = {59-69},
  year      = {2019},
  abstract  = {Unprecedented urbanization in particular in countries of the global south result in informal urban development processes, especially in mega cities. With an estimated 1 billion slum dwellers globally, the United Nations have made the fight against poverty the number one sustainable development goal. To provide better infrastructure and thus a better life to slum dwellers, detailed information on the spatial location and size of slums is of crucial importance. In the past, remote sensing has proven to be an extremely valuable and effective tool for mapping slums. The nature of used mapping approaches by machine learning, however, made it necessary to invest a lot of effort in training the models. Recent advances in deep learning allow for transferring trained fully convolutional networks (FCN) from one data set to another. Thus, in our study we aim at analyzing transfer learning capabilities of FCNs to slum mapping in various satellite images. A model trained on very high resolution optical satellite imagery from QuickBird is transferred to Sentinel-2 and TerraSAR-X data. While free-of-charge Sentinel-2 data is widely available, its comparably lower resolution makes slum mapping a challenging task. TerraSAR-X data on the other hand, has a higher resolution and is considered a powerful data source for intra-urban structure analysis. Due to the different image characteristics of SAR compared to optical data, however, transferring the model could not improve the performance of semantic segmentation but we observe very high accuracies for mapped slums in the optical data: QuickBird image obtains 86-88\% (positive prediction value and sensitivity) and a significant increase for Sentinel-2 applying transfer learning can be observed (from 38 to 55\% and from 79 to 85\% for PPV and sensitivity, respectively). Using transfer learning proofs extremely valuable in retrieving information on small-scaled urban structures such as slum patches even in satellite images of decametric resolution.},
  language  = {en}
}
@article{SenaratneMuehlbauerKiefletal.2023,
  author    = {Senaratne, Hansi and M{\"u}hlbauer, Martin and Kiefl, Ralph and C{\´a}rdenas, Andrea and Prathapan, Lallu and Riedlinger, Torsten and Biewer, Carolin and Taubenb{\"o}ck, Hannes},
  title     = {The Unseen — an investigative analysis of thematic and spatial coverage of news on the ongoing refugee crisis in West Africa},
  series = {ISPRS International Journal of Geo-Information},
  volume    = {12},
  journal   = {ISPRS International Journal of Geo-Information},
  number    = {4},
  issn      = {2220-9964},
  doi       = {10.3390/ijgi12040175},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-313607},
  year      = {2023},
  abstract  = {The fastest growing regional crisis is happening in West Africa today, with over 8 million people considered persons of concern. A culmination of identity politics, climate-driven disasters, and extreme poverty has led to this humanitarian crisis in the region and is exacerbated by a lack of political will and misplaced media attention. The current state of the art does not present sufficient investigations of the thematic and spatial coverage of news media of this crisis in this region. This paper studies the spatial coverage of this crisis as reported in the media, and the themes associated with those locations, based on a curated dataset. For the time frame 12 March to 15 September 2021, 2017 news articles related to the refugee crisis in West Africa were examined and manually coded based on (1) the geographical locations mentioned in each article; (2) the themes found in the articles in reference to a location (e.g., Relocation of people in Abuja). The dataset introduces a thematic dimension, as never achieved before, to the conflict-ridden areas in West Africa. A comparative analysis with UNHCR (United Nations High Commissioner for Refugees) data showed that 96.8\% of refugee-related locations in West Africa were not covered by news during the considered time frame. Contrastingly, 80.4\% of locations mentioned in the news do not appear in the UNHCR repository. Most news articles published during this time frame reported on Development aid or Political statements. Linear multiple regression analysis showed GDP per capita and political stability to be among the most influential determinants of news coverage.},
  language  = {en}
}