Predicting Building Functions by Fusing Social Media and Remote Sensing Data

Die Funktionen von Gebäuden lassen sich nicht direkt messen, sondern erfordern die Interpretation von Daten. In dieser Arbeit werden drei neue Methoden zur Vorhersage von Gebäudefunktionen vorgestellt, die auf Daten aus sozialen Medien und Fernerkundungsdaten beruhen. Die Methoden basieren auf Ansätzen des maschinellen Lernens und wurden auf kulturell diversifizierten Datensätzen entwickelt und getestet. Die Vorhersage lässt sich durch die Kombination mehrerer Modelle um bis zu 6,9% erhöhen

Can linguistic features extracted from geo-referenced tweets help building function classification in remote sensing?

The fusion of two or more different data sources is a widely accepted technique in remote sensing while becoming increasingly important due to the availability of big Earth Observation satellite data. As a complementary source of geo-information to satellite data, massive text messages from social media form a temporally quasi-seamless, spatially multi-perspective stream, but with unknown and diverse quality. Despite the uncontrolled quality: can linguistic features extracted from geo-referenced tweets support remote sensing tasks? This work presents a straightforward decision fusion framework for very high-resolution remote sensing images and Twitter text messages. We apply our proposed fusion framework to a land-use classification task - the building function classification task - in which we classify building functions like commercial or residential based on linguistic features derived from tweets and remote sensing images. Using building tags from OpenStreetMap (OSM), we labeled tweets and very high-resolution (VHR) images from Google Maps. We collected English tweets from San Francisco, New York City, Los Angeles, and Washington D.C. and trained a stacked bi-directional LSTM neural network with these tweets. For the aerial images, we predicted building functions with state-of-the-art Convolutional Neural Network (CNN) architectures fine-tuned from ImageNet on the given task. After predicting each modality separately, we combined the prediction probabilities of both models building-wise at a decision level. We show that the proposed fusion framework can improve the classification results of the building type classification task. To the best of our knowledge, we are the first to use semantic contents of Twitter messages and fusing them with remote sensing images to classify building functions at a single building level

Using social media images for building function classification

Urban land use on a building instance level is crucial geo-information for many applications yet challenging to obtain. Steet-level images are highly suited to predict building functions as the building façades provide clear hints. Social media image platforms contain billions of images, including but not limited to street perspectives. This study proposes a filtering pipeline to yield high-quality, ground-level imagery from large-scale social media image datasets to cope with this issue. The pipeline ensures all resulting images have complete and valid geotags with a compass direction to relate image content and spatial objects. We analyze our method on a culturally diverse social media dataset from Flickr with more than 28 million images from 42 cities worldwide. The obtained dataset is then evaluated in the context of a building function classification task with three classes: Commercial, residential, and other. Fine-tuned state-of-the-art architectures yield F1 scores of up to 0.51 on the filtered images. Our analysis shows that the quality of the labels from OpenStreetMap limits the performance. Human-validated labels increase the F1 score by 0.2. Therefore, we consider these labels weak and publish the resulting images from our pipeline and the depicted buildings as a weakly labeled datase

Zooming into Uncertainties: Towards Fusing Multi Zoom Level Imagery for Urban Land Use Segmentation

Urban land use prediction is an ill-posed problem from a remote sensing perspective. Some areas are easy to predict with aerial images, e.g. residential areas or industrial areas, whereas it is nearly impossible to predict land use in dense urban centers with highly mixed land use. In this study, we use a fully convolutional, Bayesian neural network for urban land use segmentation that yields predictions and pixel-wise uncertainty values side-by-side. By adding aleatoric uncertainty to the output of our model, we can assess how much the model benefits from the provided data. We train our network using a dataset from four metropolitan areas in the U.S. on two different zoom levels. Our results show that adding aleatoric uncertainty can improve the IoU scores if a sufficient amount of informative data is provided

So2Sat POP -- A Curated Benchmark Data Set for Population Estimation from Space on a Continental Scale

Obtaining a dynamic population distribution is key to many decision-making processes such as urban planning, disaster management and most importantly helping the government to better allocate socio-technical supply. For the aspiration of these objectives, good population data is essential. The traditional method of collecting population data through the census is expensive and tedious. In recent years, machine learning methods have been developed to estimate the population distribution. Most of the methods use data sets that are either developed on a small scale or not publicly available yet. Thus, the development and evaluation of the new methods become challenging. We fill this gap by providing a comprehensive data set for population estimation in 98 European cities. The data set comprises digital elevation model, local climate zone, land use classifications, nighttime lights in combination with multi-spectral Sentinel-2 imagery, and data from the Open Street Map initiative. We anticipate that it would be a valuable addition to the research community for the development of sophisticated machine learning-based approaches in the field of population estimation

So2Sat POP - A Curated Benchmark Data Set for Population Estimation from Space on a Continental Scale

Obtaining a dynamic population distribution is key to many decision-making processes such as urban planning, disaster management and most importantly helping the government to better allocate socio-technical supply. For the aspiration of these objectives, good population data is essential. The traditional method of collecting population data through the census is expensive and tedious. In recent years, statistical and machine learning methods have been developed to estimate population distribution. Most of the methods use data sets that are either developed on a small scale or not publicly available yet. Thus, the development and evaluation of new methods become challenging. We fill this gap by providing a comprehensive data set for population estimation in 98 European cities. The data set comprises a digital elevation model, local climate zone, land use proportions, nighttime lights in combination with multi-spectral Sentinel-2 imagery, and data from the Open Street Map initiative. We anticipate that it would be a valuable addition to the research community for the development of sophisticated approaches in the field of population estimation

Towards Large-Scale Building Attribute Mapping Using Crowdsourced Images: Scene Text Recognition on Flickr and Problems to be Solved

Crowdsourced platforms provide huge amounts of street-view images that contain valuable building information. This work addresses the challenges in applying Scene Text Recognition (STR) in crowdsourced street-view images for building attribute mapping. We use Flickr images, particularly examining texts on building facades. A Berlin Flickr dataset is created, and pre-trained STR models are used for text detection and recognition. Manual checking on a subset of STR-recognized images demonstrates high accuracy. We examined the correlation between STR results and building functions, and analysed instances where texts were recognized on residential buildings but not on commercial ones. Further investigation revealed significant challenges associated with this task, including small text regions in street-view images, the absence of ground truth labels, and mismatches in buildings in Flickr images and building footprints in OpenStreetMap (OSM). To develop city-wide mapping beyond urban hotspot locations, we suggest differentiating the scenarios where STR proves effective while developing appropriate algorithms or bringing in additional data for handling other cases. Furthermore, interdisciplinary collaboration should be undertaken to understand the motivation behind building photography and labeling. The STR-on-Flickr results are publicly available at https://github.com/ya0-sun/STR-Berlin

Geo-Information Harvesting from Social Media Data

As unconventional sources of geo-information, massive imagery and text messages from open platforms and social media form a temporally quasi-seamless, spatially multi-perspective stream, but with unknown and diverse quality. Due to its complementarity to remote sensing data, geo-information from these sources offers promising perspectives, but harvesting is not trivial due to its data characteristics. In this article, we address key aspects in the field, including data availability, analysis-ready data preparation and data management, geo-information extraction from social media text messages and images, and the fusion of social media and remote sensing data. We then showcase some exemplary geographic applications. In addition, we present the first extensive discussion of ethical considerations of social media data in the context of geo-information harvesting and geographic applications. With this effort, we wish to stimulate curiosity and lay the groundwork for researchers who intend to explore social media data for geo-applications. We encourage the community to join forces by sharing their code and data.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

Geo-Information Harvesting from Social Media Data

As unconventional sources of geo-information, massive imagery and text messages from open platforms and social media form a temporally quasi-seamless, spatially multiperspective stream, but with unknown and diverse quality. Due to its complementarity to remote sensing data, geo-information from these sources offers promising perspectives, but harvesting is not trivial due to its data characteristics. In this article, we address key aspects in the field, including data availability, analysisready data preparation and data management, geo-information extraction from social media text messages and images, and the fusion of social media and remote sensing data. We then showcase some exemplary geographic applications. In addition, we present the first extensive discussion of ethical considerations of social media data in the context of geo-information harvesting and geographic applications. With this effort, we wish to stimulate curiosity and lay the groundwork for researchers who intend to explore social media data for geo-applications. We encourage the community to join forces by sharing their code and data