@article{HoeserBachoferKuenzer2020,
  author    = {Hoeser, Thorsten and Bachofer, Felix and Kuenzer, Claudia},
  title     = {Object detection and image segmentation with deep learning on Earth Observation data: a review — part II: applications},
  series = {Remote Sensing},
  volume    = {12},
  journal   = {Remote Sensing},
  number    = {18},
  issn      = {2072-4292},
  doi       = {10.3390/rs12183053},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-213152},
  year      = {2020},
  abstract  = {In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by investigating aggregated classes. The increase in data with a very high spatial resolution enables investigations on a fine-grained feature level which can help us to better understand the dynamics of land surfaces by taking object dynamics into account. To extract fine-grained features and objects, the most popular deep-learning model for image analysis is commonly used: the convolutional neural network (CNN). In this review, we provide a comprehensive overview of the impact of deep learning on EO applications by reviewing 429 studies on image segmentation and object detection with CNNs. We extensively examine the spatial distribution of study sites, employed sensors, used datasets and CNN architectures, and give a thorough overview of applications in EO which used CNNs. Our main finding is that CNNs are in an advanced transition phase from computer vision to EO. Upon this, we argue that in the near future, investigations which analyze object dynamics with CNNs will have a significant impact on EO research. With a focus on EO applications in this Part II, we complete the methodological review provided in Part I.},
  language  = {en}
}
@article{WangBachoferKoehleretal.2022,
  author    = {Wang, Zhiyuan and Bachofer, Felix and Koehler, Jonas and Huth, Juliane and Hoeser, Thorsten and Marconcini, Mattia and Esch, Thomas and Kuenzer, Claudia},
  title     = {Spatial modelling and prediction with the spatio-temporal matrix: a study on predicting future settlement growth},
  series = {Land},
  volume    = {11},
  journal   = {Land},
  number    = {8},
  issn      = {2073-445X},
  doi       = {10.3390/land11081174},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-281856},
  year      = {2022},
  abstract  = {In the past decades, various Earth observation-based time series products have emerged, which have enabled studies and analysis of global change processes. Besides their contribution to understanding past processes, time series datasets hold enormous potential for predictive modeling and thereby meet the demands of decision makers on future scenarios. In order to further exploit these data, a novel pixel-based approach has been introduced, which is the spatio-temporal matrix (STM). The approach integrates the historical characteristics of a specific land cover at a high temporal frequency in order to interpret the spatial and temporal information for the neighborhood of a given target pixel. The provided information can be exploited with common predictive models and algorithms. In this study, this approach was utilized and evaluated for the prediction of future urban/built-settlement growth. Random forest and multi-layer perceptron were employed for the prediction. The tests have been carried out with training strategies based on a one-year and a ten-year time span for the urban agglomerations of Surat (India), Ho-Chi-Minh City (Vietnam), and Abidjan (Ivory Coast). The slope, land use, exclusion, urban, transportation, hillshade (SLEUTH) model was selected as a baseline indicator for the performance evaluation. The statistical results from the receiver operating characteristic curve (ROC) demonstrate a good ability of the STM to facilitate the prediction of future settlement growth and its transferability to different cities, with area under the curve (AUC) values greater than 0.85. Compared with SLEUTH, the STM-based model achieved higher AUC in all of the test cases, while being independent of the additional datasets for the restricted and the preferential development areas.},
  language  = {en}
}