Quantifying urban heat exposure at fine scale - modeling outdoor and indoor temperatures using citizen science and VHR remote sensing

Global warming and advancing urbanization lead to an increased heat exposure for city dwellers. Especially during summertime heatwaves, extreme daytime as well as high nighttime temperatures expose vulnerable people to potentially deadly heat risk. This applies specifically to indoor air temperatures, since people spend a lot of their time indoors. Against this background, this study relates outdoor and indoor air temperature measurements to area-wide geospatial data regarding summertime urban heat in the city of Augsburg, Germany. Air temperature data is collected from formalized as well as citizen science measurements, while remote sensing data with very-high spatial resolution (VHR) is utilized for assessment of their drivers and influencing factors. A land use regression approach is developed for city-wide modeling of outdoor and indoor air temperatures at the level of individual residential buildings. Daytime outdoor temperatures could be largely explained by vegetation parameters and imperviousness, whereas nighttime temperatures were more related to the building stock and radiation properties. For indoor temperatures, building density as well as building height and volume are additionally relevant. Outdoor air temperatures could be modeled with higher accuracies (mean absolute error (MAE) < 0.5 °C) compared to indoor temperatures (MAE < 1.5 °C), whereas outdoor and indoor modeling results are consistent with well-known patterns across different local climate zones (LCZ)

Categorizing Urban Structural Types using an Object-Based Local Climate Zone Classification Scheme in Medellín, Colombia

Climate change is reshaping societies. As we see more and more people moving to urban areas an ever-increasing number settles in low-cost and more hazardous areas. However, due to the rapid growth and sheer scale of informal settlements, knowledge gaps often exist on location or quantity. In this sense, Earth Observation combined with machine learning techniques allows to generate reliable geo-information. In this study, we classify the morphologically heterogeneous entire urban area of Medellín, Colombia into urban structural types. We do this by the Local Climate Zone (LCZ) scheme. Our specific focus is on one structural type, i.e. informal settlements. We test whether it is feasible by the LCZ concept to localize and quantify these vulnerable areas. The LCZ scheme is generic, replicable, neutral, and has become widespread in urban studies. We use urban blocks to perform a scene-based image classification into nine LCZs. We refer to multi-modal remotely-sensed data: high-resolution multispectral image data and elevation data. We apply an optimized random forest algorithm using shape metrics, as well as spectral and texture features. In general, we find the LCZ classification, measured with an overall accuracy of 82%, shows a reliable representation of urban typologies and functions across the city. Specifically, we compare the urban blocks classified as the LCZ lightweigth low-rise to the informal settlements provided by the city of Medellín. Here we reach an agreement of 86%. Besides, our approach complements the official dataset by including recently developed areas which are not yet considered by the city

The growing threat: Earth Observation for reducing landslide risk from climate change

In this paper a specific case study is introduced illustrating how Earth Observation (EO) can be used to assess exposure and vulnerability to landslide hazards and to strengthen resilience. Combining heterogeneous EO data can greatly improve knowledge on natural hazard risks for many urban dwellers

Multitemporal landslide exposure and vulnerability assessment in Medellín, Colombia

Landslides are often deadly natural events. Steep slopes and certain loose soil types are predestined areas for them. Moreover, in the context of climate change, extreme weather events such as heavy rainfall, which often trigger landslides, are becoming even more likely. While all this is well known, it, therefore, stands to reason that this knowledge will lead to the avoidance of these risks. On the other hand, however, there are highly dynamic urbanization processes that often overtake formal urban planning processes by rising population figures and areal expansion. In the course of these processes, economically-deprived population groups often have no other option than to informally build on high-risk areas. Against these backgrounds, we systematically examine in this study how these risks develop over a 24-year period from 1994 to 2018 taking into account three time steps, with respect to the city-wide exposure and in particular with respect to different social groups. For this purpose, we use heterogeneous input data from remote sensing, landslide hazard maps, and census data. Our case study is the city of Medellín in Colombia. We develop and apply a set of methods integrating the heterogenous data sets to map, quantify and monitor exposure and social vulnerability at a fine spatial granularity. Our results document first of all the highly dynamic growth in total population and urban areas. However, our results reveal that the city's expansion is socially unevenly distributed. People of higher vulnerability proxied by informal settlements are found to settle in considerably higher shares of areas exposed to landslides. This study proposes a methodological set-up that allows for monitoring exposure and social vulnerability over long time spans at a fine spatial resolution, allows to bring inequality into the spotlight, and provides decision-makers with better information to develop socially responsible policies

Der Einsatz von Distant Reading auf einem Korpus deutschsprachiger Songtexte

Wir präsentieren die ersten Ergebnisse eines Projekts zur Exploration des Einsatzes von computergestützter Textanalyse und Distant Reading auf einem Korpus deutschsprachiger Songtexte. Der Fokus liegt dabei momentan vor allem auf der Identifikation genrespezifischer Unterschiede für die Genres Pop, Rap, Rock und Schlager. Zu diesem Zweck wurde ein Korpus bestehend aus 4636 Songtexten einiger der bekanntesten Genrevertreter seit den 60er Jahren über die Plattform LyricWiki akquiriert. Es werden erste punktuelle Ergebnisse bezüglich Wortfrequenzanalysen, Sentiment Analysis und Topic Modeling präsentiert und diskutiert. Die Wortverteilungen weisen eine homogene Verteilung von in allen Genres auftretenden Konzepten auf, lediglich Rap grenzt sich stärker ab. Ähnliches zeigt sich für die Methoden der Sentiment Analysis und des Topic Modeling. Auch hier werden Unterschiede bezüglich der Verwendung sentiment-beladener Wörter und der Konstitution von Topics insbesondere bezüglich des Genres Rap deutlich

Revealing landslisde exposure of informal settlements in Medellín using Deep Learning

Large areas of informal settlements on the slopes of Medellín are exposed to landslide risk, but there exists no accurate and up-to date data set on the location and size of informal areas. It is thus difficult to develop mitigation strategies to reduce the risk for the local population. Here, we tackle the issue of inaccurate geodata and apply a CNN for the extraction of individual building footprints from orthophotos. With it we achieve a more reliable data base for a more precise estimation of the amount of exposed population in informal areas towards landslides

Empiric recommendations for population disaggregation under different data scenarios

High-resolution population mapping is of high relevance for developing and implementing tailored actions in several fields: From decision making in crisis management to urban planning. Earth Observation has considerably contributed to the development of methods for disaggregating population figures with higher resolution data into fine-grained population maps. However, which method is most suitable on the basis of the available data, and how the spatial units and accuracy metrics affect the validation process is not fully known. We aim to provide recommendations to researches that attempt to produce high-resolution population maps using remote sensing and geospatial information in heterogeneous urban landscapes. For this purpose, we performed a comprehensive experimental research on population disaggregation methods with thirty-six different scenarios. We combined five different top-down methods (from basic to complex, i.e., binary and categorical dasymetric, statistical, and binary and categorical hybrid approaches) on different subsets of data with diverse resolutions and degrees of availability (poor, average and rich). Then, the resulting population maps were systematically validated with a two-fold approach using six accuracy metrics. We found that when only using remotely sensed data the combination of statistical and dasymetric methods provide better results, while highly-resolved data require simpler methods. Besides, the use of at least three relative accuracy metrics is highly encouraged since the validation depends on level and method. We also analysed the behaviour of relative errors and how they are affected by the heterogeneity of the urban landscape. We hope that our recommendations save additional efforts and time in future population mapping

Grosswohnsiedlungen der Zukunft

Der steigende Wohnungsdruck und in die Höhe schießende Miet- und Kaufpreise in den Städten sowie die aktuelle Energie- und sich verschärfende Klimakrise, machen ein Nachund Umdenken über das Bauen und Wohnen der Zukunft nötig: Nach einer aktuellen Studie im Auftrag des Westdeutschen Rundfunks (WDR) ist jede:r zweite Befragte von einer starken oder sehr starken Steigerung der Wohnausgaben, im Vergleichszeitraum der letzten drei Jahre, betroffen (vgl. Zeit Online 2022). Um diesen Wohnungsdruck langfristig abzumildern verfolgt die aktuelle Ampelkoalition das Ziel der Errichtung von 400.000 neuen Wohnungen pro Jahr (vgl. Wegener 2022). Die hohen Baukosten sollen dabei mithilfe der Förderung serieller und modularer Bauweise begrenzt werden. Zugleich erfordern die politischen Ziele einer nachhaltigen und klimaverträglichen Stadtentwicklung deutliche Anstrengungen zum Flächensparen: Laut dem Klimaschutzplan der Bundesregierung soll der Flächenverbrauch bis 2050 auf „Netto-Null“ reduziert und der Übergang in eine sogenannte Flächenkreislaufwirtschaft vollzogen sein (vgl. Umweltbundesamt 2022a). Das bedeutet, dass bis 2050 keine neue Siedlungs- und Verkehrsfläche mehr in Anspruch genommen werden, sondern bauliche Entwicklungen insbesondere auf bereits versiegelten oder Brachflächen stattfinden sollen. Weitere politische Zielsetzungen wie die Mobilitäts- und Energiewende im Sinne einer klimagerechten Transformation sowie die Stärkung der sozialen Teilhabe und Gerechtigkeit in unseren Städten (vgl. SPD, BÜNDNIS 90/DIE GRÜNEN, FDP 2021) erfordern ebenfalls neue städtebauliche Ansätze und Konzepte, sowohl mit Blick auf die Weiterentwicklung der Bestände als auch für neue Gebäude und Quartiere. Vor diesem Hintergrund gewinnen nicht nur das Thema der klima- und sozialgerechten Bestandsentwicklung, sondern auch das des „großen Bauens der Zukunft“, ursprünglich ein Kind des mittleren und späten 20. Jahrhunderts, heute wieder verstärkt an Bedeutung. Die vorliegende Studie „Großwohnsiedlungen der Zukunft“ nahm daher den Quartierstypus der Großwohnsiedlungen in den Blick und untersuchte ihn auf sein Potenzial als Baustein einer nachhaltigen und zukunftsfähigen Stadtentwicklung. Dazu ging das Projekt der Frage nach, was attraktive Großwohnsiedlungen ausmacht und wie eine Transformation der Quartiere mit Blick auf die genannten Zielsetzungen aussehen kann. Zu diesem Zweck wurden bestehende Siedlungen aus den 1960er, 70er und 80er Jahren betrachtet und vor allem auch die nachgewiesene Diskrepanz zwischen Innen- und Außenwahrnehmung der Quartiere untersucht: Warum werden die Siedlungen von Außenstehenden oft negativ wahrgenommen, während sie von ihren Bewohner:innen überwiegend sehr geschätzt werden? Worin bestehen die Qualitäten der Großwohnsiedlungen, welche Faktoren tragen zur Wohnzufriedenheit bei? Welche Defizite und Entwicklungspotenziale sind darüber hinaus erkennbar? Darauf aufbauend wurden Erkenntnisse erarbeitet, wie bestehende Siedlungen nachhaltig weiterentwickelt und transformiert werden können oder welche städtebaulichen, freiraumplanerischen oder auch stadtentwicklungspolitischen Leitlinien eine neue Großwohnsiedlung verfolgen sollte um attraktiven und zukunftsfähigen Wohnraum zu generieren. Der vorliegende Abschlussbericht führt die dadurch gewonnenen Erkenntnisse zusammen und gibt einen Überblick über mögliche Handlungsansätze

Deep learning-based denoising streamed from mobile phones improves speech-in-noise understanding for hearing aid users

The hearing loss of almost half a billion people is commonly treated with hearing aids. However, current hearing aids often do not work well in real-world noisy environments. We present a deep learning based denoising system that runs in real time on iPhone 7 and Samsung Galaxy S10 (25ms algorithmic latency). The denoised audio is streamed to the hearing aid, resulting in a total delay of around 75ms. In tests with hearing aid users having moderate to severe hearing loss, our denoising system improves audio across three tests: 1) listening for subjective audio ratings, 2) listening for objective speech intelligibility, and 3) live conversations in a noisy environment for subjective ratings. Subjective ratings increase by more than 40%, for both the listening test and the live conversation compared to a fitted hearing aid as a baseline. Speech reception thresholds, measuring speech understanding in noise, improve by 1.6 dB SRT. Ours is the first denoising system that is implemented on a mobile device, streamed directly to users' hearing aids using only a single channel as audio input while improving user satisfaction on all tested aspects, including speech intelligibility. This includes overall preference of the denoised and streamed signal over the hearing aid, thereby accepting the higher latency for the significant improvement in speech understanding