TY - JOUR A1 - Lacombe, Amanda Meneses Ferreira A1 - Soares, Iberê Cauduro A1 - Mariani, Beatriz Marinho de Paula A1 - Nishi, Mirian Yumie A1 - Bezerra-Neto, João Evangelista A1 - Charchar, Helaine da Silva A1 - Brondani, Vania Balderrama A1 - Tanno, Fabio A1 - Srougi, Victor A1 - Chambo, José Luiz A1 - Costa de Freitas, Ricardo Miguel A1 - Mendonca, Berenice Bilharinho A1 - Hoff, Ana O. A1 - Almeida, Madson Q. A1 - Weigand, Isabel A1 - Kroiss, Matthias A1 - Zerbini, Maria Claudia Nogueira A1 - Fragoso, Maria Candida Barisson Villares T1 - Sterol O-acyl transferase 1 as a prognostic marker of adrenocortical carcinoma JF - Cancers N2 - Adrenocortical carcinoma (ACC) is a rare endocrine malignancy with an unfavorable prognosis. Despite the poor prognosis in the majority of patients, no improvements in treatment strategies have been achieved. Therefore, the discovery of new prognostic biomarkers is of enormous interest. Sterol-O-acyl transferase 1 (SOAT1) is involved in cholesterol esterification and lipid droplet formation. Recently, it was demonstrated that SOAT1 inhibition leads to impaired steroidogenesis and cell viability in ACC. To date, no studies have addressed the impact of SOAT1 expression on ACC prognosis and clinical outcomes. We evaluated SOAT1 expression by quantitative real-time polymerase chain reaction and immunohistochemistry in a tissue microarray of 112 ACCs (Weiss score ≥ 3) from adults treated in a single tertiary center in Brazil. Two independent pathologists evaluated the immunohistochemistry results through a semiquantitative approach (0–4). We aimed to evaluate the correlation between SOAT1 expression and clinical, biochemical and anatomopathological parameters, recurrence-free survival (RFS), progression-free survival (PFS), and overall survival (OS). SOAT1 protein expression was heterogeneous in this cohort, 37.5% of the ACCs demonstrated a strong SOAT1 protein expression (score > 2), while 62.5% demonstrated a weak or absent protein expression (score ≤ 2). Strong SOAT1 protein expression correlated with features of high aggressiveness in ACC, such as excessive tumor cortisol secretion (p = 0.01), an advanced disease stage [European Network for the Study of Adrenal Tumors (ENSAT) staging system 3 and 4 (p = 0.011)] and a high Ki67 index (p = 0.002). In multivariate analysis, strong SOAT1 protein expression was an independent predictor of a reduced OS (hazard ratio (HR) 2.15, confidence interval (CI) 95% 1.26–3.66; p = 0.005) in all patients (n = 112), and a reduced RFS (HR 2.1, CI 95% 1.09–4.06; p = 0.027) in patients with localized disease at diagnosis (n = 83). Our findings demonstrated that SOAT1 protein expression has prognostic value in ACC and reinforced the importance of investigating SOAT1 as a possible therapeutic target for patients with ACC. KW - adrenocortical carcinoma KW - prognostic factors KW - SOAT1 KW - target therapies Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-200857 SN - 2072-6694 VL - 12 IS - 1 ER - TY - JOUR A1 - Weigand, Matthias A1 - Wurm, Michael A1 - Dech, Stefan A1 - Taubenböck, Hannes T1 - Remote sensing in environmental justice research—a review JF - ISPRS International Journal of Geo-Information N2 - 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. KW - satellite remote sensing KW - review KW - environmental justice KW - big earth data KW - urban environments Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-196950 SN - 2220-9964 VL - 8 IS - 1 ER - TY - JOUR A1 - Weigand, Annika A1 - Boos, Anja M. A1 - Tasbihi, Kereshmeh A1 - Beier, Justus P. A1 - Dalton, Paul D. A1 - Schrauder, Michael A1 - Horch, Raymund E. A1 - Beckmann, Matthias W. A1 - Strissel, Pamela L. A1 - Strick, Reiner T1 - Selective isolation and characterization of primary cells from normal breast and tumors reveal plasticity of adipose derived stem cells JF - Breast Cancer Research N2 - Background There is a need to establish more cell lines from breast tumors in contrast to immortalized cell lines from metastatic effusions in order to represent the primary tumor and not principally metastatic biology of breast cancer. This investigation describes the simultaneous isolation, characterization, growth and function of primary mammary epithelial cells (MEC), mesenchymal cells (MES) and adipose derived stem cells (ADSC) from four normal breasts, one inflammatory and one triple-negative ductal breast tumors. Methods A total of 17 cell lines were established and gene expression was analyzed for MEC and MES (n = 42) and ADSC (n = 48) and MUC1, pan-KRT, CD90 and GATA-3 by immunofluorescence. DNA fingerprinting to track cell line identity was performed between original primary tissues and isolates. Functional studies included ADSC differentiation, tumor MES and MEC invasion co-cultured with ADSC-conditioned media (CM) and MES adhesion and growth on 3D-printed scaffolds. Results Comparative analysis showed higher gene expression of EPCAM, CD49f, CDH1 and KRTs for normal MEC lines; MES lines e.g. Vimentin, CD10, ACTA2 and MMP9; and ADSC lines e.g. CD105, CD90, CDH2 and CDH11. Compared to the mean of all four normal breast cell lines, both breast tumor cell lines demonstrated significantly lower ADSC marker gene expression, but higher expression of mesenchymal and invasion gene markers like SNAI1 and MMP2. When compared with four normal ADSC differentiated lineages, both tumor ADSC showed impaired osteogenic and chondrogenic but enhanced adipogenic differentiation and endothelial-like structures, possibly due to high PDGFRB and CD34. Addressing a functional role for overproduction of adipocytes, we initiated 3D-invasion studies including different cell types from the same patient. CM from ADSC differentiating into adipocytes induced tumor MEC 3D-invasion via EMT and amoeboid phenotypes. Normal MES breast cells adhered and proliferated on 3D-printed scaffolds containing 20 fibers, but not on 2.5D-printed scaffolds with single fiber layers, important for tissue engineering. Conclusion Expression analyses confirmed successful simultaneous cell isolations of three different phenotypes from normal and tumor primary breast tissues. Our cell culture studies support that breast-tumor environment differentially regulates tumor ADSC plasticity as well as cell invasion and demonstrates applications for regenerative medicine. KW - Normal breast KW - Breast cancer KW - Stem cells plasticity KW - Primary cell lines KW - Tissue engineering Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-164759 VL - 18 IS - 32 ER - TY - JOUR A1 - Eckhardt, Carolin A1 - Sbiera, Iuliu A1 - Krebs, Markus A1 - Sbiera, Silviu A1 - Spahn, Martin A1 - Kneitz, Burkhard A1 - Joniau, Steven A1 - Fassnacht, Martin A1 - Kübler, Hubert A1 - Weigand, Isabel A1 - Kroiss, Matthias T1 - High expression of Sterol-O-Acyl transferase 1 (SOAT1), an enzyme involved in cholesterol metabolism, is associated with earlier biochemical recurrence in high risk prostate cancer JF - Prostate Cancer and Prostatic Diseases N2 - Background Prostate cancer (PCa) is the most frequent cancer in men. The prognosis of PCa is heterogeneous with many clinically indolent tumors and rare highly aggressive cases. Reliable tissue markers of prognosis are lacking. Active cholesteryl ester synthesis has been associated with prostate cancer aggressiveness. Sterol-O-Acyl transferases (SOAT) 1 and 2 catalyze cholesterol esterification in humans. Objective To investigate the value of SOAT1 and SOAT2 tissue expression as prognostic markers in high risk PCa. Patients and Methods Formalin-fixed paraffin-embedded tissue samples from 305 high risk PCa cases treated with radical prostatectomy were analyzed for SOAT1 and SOAT2 protein expression by semi-quantitative immunohistochemistry. The Kaplan-Meier method and Cox proportional hazards modeling were used to compare outcome. Main Outcome Measure Biochemical recurrence (BCR) free survival. Results SOAT1 expression was high in 73 (25%) and low in 219 (75%; not evaluable: 13) tumors. SOAT2 was highly expressed in 40 (14%) and at low levels in 249 (86%) samples (not evaluable: 16). By Kaplan-Meier analysis, we found significantly shorter median BCR free survival of 93 months (95% confidence interval 23.6-123.1) in patients with high SOAT1 vs. 134 months (112.6-220.2, Log-rank p < 0.001) with low SOAT1. SOAT2 expression was not significantly associated with BCR. After adjustment for age, preoperative PSA, tumor stage, Gleason score, resection status, lymph node involvement and year of surgery, high SOAT1 but not SOAT2 expression was associated with shorter BCR free survival with a hazard ratio of 2.40 (95% CI 1.57-3.68, p < 0.001). Time to clinical recurrence and overall survival were not significantly associated with SOAT1 and SOAT2 expression CONCLUSIONS: SOAT1 expression is strongly associated with BCR free survival alone and after multivariable adjustment in high risk PCa. SOAT1 may serve as a histologic marker of prognosis and holds promise as a future treatment target. KW - prostate cancer KW - SOAT1 KW - cholesterol metabolism Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-271819 SN - 1476-5608 VL - 25 IS - 3 ER - TY - JOUR A1 - Weigand, Isabel A1 - Ronchi, Cristina L. A1 - Vanselow, Jens T. A1 - Bathon, Kerstin A1 - Lenz, Kerstin A1 - Herterich, Sabine A1 - Schlosser, Andreas A1 - Kroiss, Matthias A1 - Fassnacht, Martin A1 - Calebiro, Davide A1 - Sbiera, Silviu T1 - PKA Cα subunit mutation triggers caspase-dependent RIIβ subunit degradation via Ser\(^{114}\) phosphorylation JF - Science Advances N2 - Mutations in the PRKACA gene are the most frequent cause of cortisol-producing adrenocortical adenomas leading to Cushing’s syndrome. PRKACA encodes for the catalytic subunit α of protein kinase A (PKA). We already showed that PRKACA mutations lead to impairment of regulatory (R) subunit binding. Furthermore, PRKACA mutations are associated with reduced RIIβ protein levels; however, the mechanisms leading to reduced RIIβ levels are presently unknown. Here, we investigate the effects of the most frequent PRKACA mutation, L206R, on regulatory subunit stability. We find that Ser\(^{114}\) phosphorylation of RIIβ is required for its degradation, mediated by caspase 16. Last, we show that the resulting reduction in RIIβ protein levels leads to increased cortisol secretion in adrenocortical cells. These findings reveal the molecular mechanisms and pathophysiological relevance of the R subunit degradation caused by PRKACA mutations, adding another dimension to the deregulation of PKA signaling caused by PRKACA mutations in adrenal Cushing’s syndrome. KW - mutation triggers KW - phosphorylation Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-270445 VL - 7 IS - 8 ER - TY - JOUR A1 - Landwehr, Laura-Sophie A1 - Altieri, Barbara A1 - Schreiner, Jochen A1 - Sbiera, Iuliu A1 - Weigand, Isabel A1 - Kroiss, Matthias A1 - Fassnacht, Martin A1 - Sbiera, Silviu T1 - Interplay between glucocorticoids and tumor-infiltrating lymphocytes on the prognosis of adrenocortical carcinoma JF - Journal for ImmunoTherapy of Cancer N2 - Background Adrenocortical carcinoma (ACC) is a rare endocrine malignancy. Tumor-related glucocorticoid excess is present in similar to 60% of patients and associated with particularly poor prognosis. Results of first clinical trials using immune checkpoint inhibitors were heterogeneous. Here we characterize tumor-infiltrating T lymphocytes (TILs) in ACC in association with glucocorticoids as potential explanation for resistance to immunotherapy. Methods We performed immunofluorescence analysis to visualize tumor-infiltrating T cells (CD3\(^+\)), T helper cells (CD3\(^+\)CD4\(^+\)), cytotoxic T cells (CD3\(^+\)CD8\(^+\)) and regulatory T cells (Tregs; CD3\(^+\)CD4\(^+\)FoxP3\(^+\)) in 146 ACC tissue specimens (107 primary tumors, 16 local recurrences, 23 metastases). Quantitative data of immune cell infiltration were correlated with clinical data (including glucocorticoid excess). Results 86.3% of ACC specimens showed tumor infiltrating T cells (7.7 cells/high power field (HPF)), including T helper (74.0%, 6.7 cells/HPF), cytotoxic T cells (84.3%, 5.7 cells/HPF) and Tregs (49.3%, 0.8 cells/HPF). The number of TILs was associated with better overall survival (HR for death: 0.47, 95% CI 0.25 to 0.87), which was true for CD4\(^+\)- and CD8\(^+\) subpopulations as well. In localized, non-metastatic ACC, the favorable impact of TILs on overall and recurrence-free survival was manifested even independently of ENSAT (European Network for the Study of Adrenal Tumors) stage, resection status and Ki67 index. T helper cells were negatively correlated with glucocorticoid excess (Phi=-0.290, p=0.009). Patients with glucocorticoid excess and low TILs had a particularly poor overall survival (27 vs. 121 months in patients with TILs without glucocorticoid excess). Conclusion Glucocorticoid excess is associated with T cell depletion and unfavorable prognosis. To reactivate the immune system in ACC by checkpoint inhibitors, an inhibition of adrenal steroidogenesis might be pivotal and should be tested in prospective studies. KW - immunity KW - immunotherapy KW - lymphocytes KW - tumor-infiltrating KW - t-lymphocytes KW - tumor microenvironment Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-229893 VL - 8 ER - TY - THES A1 - Weigand, Matthias Johann T1 - Fernerkundung und maschinelles Lernen zur Erfassung von urbanem Grün - Eine Analyse am Beispiel der Verteilungsgerechtigkeit in Deutschland T1 - Remote Sensing and Machine Learning to Capture Urban Green – An Analysis Using the Example of Distributive Justice in Germany N2 - Grünflächen stellen einen der wichtigsten Umwelteinflüsse in der Wohnumwelt der Menschen dar. Einerseits wirken sie sich positiv auf die physische und mentale Gesundheit der Menschen aus, andererseits können Grünflächen auch negative Wirkungen anderer Faktoren abmildern, wie beispielsweise die im Laufe des Klimawandels zunehmenden Hitzeereignisse. Dennoch sind Grünflächen nicht für die gesamte Bevölkerung gleichermaßen zugänglich. Bestehende Forschung im Kontext der Umweltgerechtigkeit (UG) konnte bereits aufzeigen, dass unterschiedliche sozio-ökonomische und demographische Gruppen der deutschen Bevölkerung unterschiedlichen Zugriff auf Grünflächen haben. An bestehenden Analysen von Umwelteinflüssen im Kontext der UG wird kritisiert, dass die Auswertung geographischer Daten häufig auf zu stark aggregiertem Level geschieht, wodurch lokal spezifische Expositionen nicht mehr genau abgebildet werden. Dies trifft insbesondere für großflächig angelegte Studien zu. So werden wichtige räumliche Informationen verloren. Doch moderne Erdbeobachtungs- und Geodaten sind so detailliert wie nie und Methoden des maschinellen Lernens ermöglichen die effiziente Verarbeitung zur Ableitung höherwertiger Informationen. Das übergeordnete Ziel dieser Arbeit besteht darin, am Beispiel von Grünflächen in Deutschland methodische Schritte der systematischen Umwandlung umfassender Geodaten in relevante Geoinformationen für die großflächige und hochaufgelöste Analyse von Umwelteigenschaften aufzuzeigen und durchzuführen. An der Schnittstelle der Disziplinen Fernerkundung, Geoinformatik, Sozialgeographie und Umweltgerechtigkeitsforschung sollen Potenziale moderner Methoden für die Verbesserung der räumlichen und semantischen Auflösung von Geoinformationen erforscht werden. Hierfür werden Methoden des maschinellen Lernens eingesetzt, um Landbedeckung und -nutzung auf nationaler Ebene zu erfassen. Diese Entwicklungen sollen dazu beitragen bestehende Datenlücken zu schließen und Aufschluss über die Verteilungsgerechtigkeit von Grünflächen zu bieten. Diese Dissertation gliedert sich in drei konzeptionelle Teilschritte. Im ersten Studienteil werden Erdbeobachtungsdaten der Sentinel-2 Satelliten zur deutschlandweiten Klassifikation von Landbedeckungsinformationen verwendet. In Kombination mit punktuellen Referenzdaten der europaweiten Erfassung für Landbedeckungs- und Landnutzungsinformationen des Land Use and Coverage Area Frame Survey (LUCAS) wird ein maschinelles Lernverfahren trainiert. In diesem Kontext werden verschiedene Vorverarbeitungsschritte der LUCAS-Daten und deren Einfluss auf die Klassifikationsgenauigkeit beleuchtet. Das Klassifikationsverfahren ist in der Lage Landbedeckungsinformationen auch in komplexen urbanen Gebieten mit hoher Genauigkeit abzuleiten. Ein Ergebnis des Studienteils ist eine deutschlandweite Landbedeckungsklassifikation mit einer Gesamtgenauigkeit von 93,07 %, welche im weiteren Verlauf der Arbeit genutzt wird, um grüne Landbedeckung (GLC) räumlich zu quantifizieren. Im zweiten konzeptionellen Teil der Arbeit steht die differenzierte Betrachtung von Grünflächen anhand des Beispiels öffentlicher Grünflächen (PGS), die häufig Gegenstand der UG-Forschung ist, im Vordergrund. Doch eine häufig verwendete Quelle für räumliche Daten zu öffentlichen Grünflächen, der European Urban Atlas (EUA), wird bisher nicht flächendeckend für Deutschland erhoben. Dieser Studienteil verfolgt einen datengetriebenen Ansatz, die Verfügbarkeit von öffentlichem Grün auf der räumlichen Ebene von Nachbarschaften für ganz Deutschland zu ermitteln. Hierfür dienen bereits vom EUA erfasste Gebiete als Referenz. Mithilfe einer Kombination von Erdbeobachtungsdaten und Informationen aus dem OpenStreetMap-Projekt wird ein Deep Learning -basiertes Fusionsnetzwerk erstellt, welche die verfügbare Fläche von öffentlichem Grün quantifiziert. Das Ergebnis dieses Schrittes ist ein Modell, welches genutzt wird, um die Menge öffentlicher Grünflächen in der Nachbarschaft zu schätzen (𝑅 2 = 0.952). Der dritte Studienteil greift die Ergebnisse der ersten beiden Studienteile auf und betrachtet die Verteilung von Grünflächen in Deutschland unter Hinzunahme von georeferenzierten Bevölkerungsdaten. Diese exemplarische Analyse unterscheidet dabei Grünflächen nach zwei Typen: GLC und PGS. Zunächst wird mithilfe deskriptiver Statistiken die generelle Grünflächenverteilung in der Bevölkerung Deutschlands beleuchtet. Daraufhin wird die Verteilungsgerechtigkeit anhand gängiger Gerechtigkeitsmetriken bestimmt. Abschließend werden die Zusammenhänge zwischen der demographischen Komposition der Nachbarschaft und der verfügbaren Menge von Grünflächen anhand dreier exemplarischer soziodemographischer Gesellschaftsgruppen untersucht. Die Analyse zeigt starke Unterschiede der Verfügbarkeit von PGS zwischen städtischen und ländlichen Gebieten. Ein höherer Prozentsatz der Stadtbevölkerung hat Zugriff das Mindestmaß von PGS gemessen an der Vorgabe der Weltgesundheitsorganisation. Die Ergebnisse zeigen auch einen deutlichen Unterschied bezüglich der Verteilungsgerechtigkeit zwischen GLC und PGS und verdeutlichen die Relevanz der Unterscheidung von Grünflächentypen für derartige Untersuchungen. Die abschließende Betrachtung verschiedener Bevölkerungsgruppen arbeitet Unterschiede auf soziodemographischer Ebene auf. In der Zusammenschau demonstriert diese Arbeit wie moderne Geodaten und Methoden des maschinellen Lernens genutzt werden können bisherige Limitierungen räumlicher Datensätze zu überwinden. Am Beispiel von Grünflächen in der Wohnumgebung der Bevölkerung Deutschlands wird gezeigt, dass landesweite Analysen zur Umweltgerechtigkeit durch hochaufgelöste und lokal feingliedrige geographische Informationen bereichert werden können. Diese Arbeit verdeutlicht, wie die Methoden der Erdbeobachtung und Geoinformatik einen wichtigen Beitrag leisten können, die Ungleichheit der Wohnumwelt der Menschen zu identifizieren und schlussendlich den nachhaltigen Siedlungsbau in Form von objektiven Informationen zu unterstützen und überwachen. N2 - Green spaces are one of the most important environmental factors for humans in the living environment. On the one hand they provide benefits to people’s physical and mental health, on the other hand they allow for the mitigation of negative impacts of environmental stressors like heat waves which are increasing as a result of climate change. Yet, green spaces are not equally accessible to all people. Existing literature in the context of Environmental Justice (EJ) research has shown that the access to green space varies among different socio-economic and demographic groups in Germany. However, previous studies in the context of EJ were criticized for using strongly spatially aggregated data for their analyses resulting in a loss of spatial detail on local environmental exposure metrics. This is especially true for large-scale studies where important spatial information often get lost. In this context, modern earth observation and geospatial data are more detailed than ever, and machine learning methods enable efficient processing to derive higher value information for diverse applications. The overall objective of this work is to demonstrate and implement methodological steps that allow for the transformation of vast geodata into relevant geoinformation for the large-scale and high-resolution analysis of environmental characteristics using the example of green spaces in Germany. By bridging the disciplines remote sensing, geoinformatics, social geography and environmental justice research, potentials of modern methods for the improvement of spatial and semantic resolution of geoinformation are explored. For this purpose, machine learning methods are used to map land cover and land use on a national scale. These developments will help to close existing data gaps and provide information on the distributional equity of green spaces. This dissertation comprises three conceptual steps. In the first part of the study, earth observation data from the Sentinel-2 satellites are used to derive land cover information across Germany. In combination with point reference data on land cover and land use from the paneuropean Land Use and Coverage Area Frame Survey (LUCAS) a machine learning model is trained. Therein, different preprocessing steps of the LUCAS data and their influence on the classification accuracy are highlighted. The classification model derives land cover information with high accuracy even in complex urban areas. One result of the study is a Germany-wide land cover classification with an overall accuracy of 93.07 % which is used in the further course of the dissertation to spatially quantify green land cover (GLC). The second conceptual part of this study focuses on the semantic differentiation of green spaces using the example of public green spaces (PGS), which is often the subject of EJ research. A frequently used source of spatial data on public green spaces, the European Urban Atlas (EUA),however, is not available for all of Germany. This part of the study takes a data-driven approach to determine the availability of public green space at the spatial level of neighborhoods for all of Germany. For this purpose, areas already covered by the EUA serve as a reference. Using a combination of earth observation data and information from the OpenStreetMap project, a Deep Learning -based fusion network is created that quantifies the available area of public green space. The result of this step is a model that is utilized to estimate the amount of public green space in the neighborhood (𝑅 2 = 0.952). The third part of this dissertation builds upon the results of the first two parts and integrates georeferenced population data to study the socio-spatial distribution of green spaces in Germany. This exemplary analysis distinguishes green spaces according to two types: GLC and PGS. In this,first, descriptive statistics are used to examine the overall distribution of green spaces available to the German population. Then, the distributional equality is determined using established equality metrics. Finally, the relationships between the demographic composition of the neighborhood and the available amount of green space are examined using three exemplary sociodemographic groups. The analysis reveals strong differences in PGS availability between urban and rural areas. Compared to the rural population, a higher percentage of the urban population has access to the minimum level of PGS defined as a target by the World Health Organization (WHO). The results also show a clear deviation in terms of distributive equality between GLC and PGS, highlighting the relevance of distinguishing green space types for such studies. The final analysis of certain population groups addresses differences at the sociodemographic level. In summary, this dissertation demonstrates how previous limitations of spatial datasets can be overcome through a combination of modern geospatial data and machine learning methods. Using the example of green spaces in the residential environment of the population in Germany,it is shown that nationwide analyses of environmental justice can be enriched by high-resolution and locally fine-grained geographic information. This study illustrates how earth observation and methods of geoinformatics can make an important contribution to identifying inequalities in people’s living environment. Such objective information can ultimately be deployed to support and monitor sustainable urban development. KW - Geografie KW - Fernerkundung KW - Maschinelles Lernen KW - Deep learning KW - Urbanes Grün KW - urban green KW - machine learning KW - distributive justice KW - environmental justice KW - Deutschland KW - Germany KW - Verteilungsgerechtigkeit KW - Umweltgerechtigkeit Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-349610 ER -