@article{semreferee,
title = {Semantic Referee: A Neural-Symbolic Framework for Enhancing Geospatial Semantic Segmentation},
author = {Marjan Alirezaie and Martin Längkvist and Michael Sioutis and Amy Loutfi},
doi = {10.3233/SW-190362},
year = {2019},
date = {2019-09-26},
journal = {Semantic Web Journal},
volume = {10},
number = {5},
pages = {863-880},
abstract = {Understanding why machine learning algorithms may fail is usually the task of the human expert that uses domain knowledge and contextual information to discover systematic shortcomings in either the data or the algorithm. In this paper, we propose a semantic referee, which is able to extract qualitative features of the errors emerging from deep machine learning frameworks and suggest corrections. The semantic referee relies on ontological reasoning about spatial knowledge in order to characterize errors in terms of their spatial relations with the environment. Using semantics, the reasoner interacts with the learning algorithm as a supervisor. In this paper, the proposed method of the interaction between a neural network classifier and a semantic referee shows how to improve the performance of semantic segmentation for satellite imagery data.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{s17112545,
title = {An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring},
author = {Marjan Alirezaie and Andrey Kiselev and Martin Längkvist and Franziska Klügl and Amy Loutfi},
url = {http://www.mdpi.com/1424-8220/17/11/2545},
doi = {10.3390/s17112545},
issn = {1424-8220},
year = {2017},
date = {2017-11-05},
journal = {Sensors},
volume = {17},
number = {11},
abstract = {This paper presents a framework in which satellite images are classified and augmented with additional semantic information to enable queries about what can be found on the map at a particular location, but also about paths that can be taken. This is achieved by a reasoning framework based on qualitative spatial reasoning that is able to find answers to high level queries that may vary on the current situation. This framework called SemCityMap, provides the full pipeline from enriching the raw image data with rudimentary labels to the integration of a knowledge representation and reasoning methods to user interfaces for high level querying. To illustrate the utility of SemCityMap in a disaster scenario, we use an urban environment—central Stockholm—in combination with a flood simulation. We show that the system provides useful answers to high-level queries also with respect to the current flood status. Examples of such queries concern path planning for vehicles or retrieval of safe regions such as “find all regions close to schools and far from the flooded area”. The particular advantage of our approach lies in the fact that ontological information and reasoning is explicitly integrated so that queries can be formulated in a natural way using concepts on appropriate level of abstraction, including additional constraints.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{langkvist_classification_2016,
title = {Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks},
author = { Martin Längkvist and Andrey Kiselev and Marjan Alirezaie and Amy Loutfi},
url = {http://www.mdpi.com/2072-4292/8/4/329},
year = {2016},
date = {2016-01-01},
journal = {Remote Sensing},
volume = {8},
number = {4},
keywords = {},
pubstate = {published},
tppubtype = {article}
}