In this paper, we propose a Stack Auto-encoder (SAE)-Driven and Semi-Supervised (SSL)-Based Deep Neural Network (DNN) to extract buildings from relatively low-cost satellite near infrared images. The novelty of our scheme is that we employ only an extremely small portion of labeled data for training the deep model which constitutes less than 0.08% of the total data. This way, we significantly reduce the manual effort needed to complete an annotation process, and thus the time required for creating a reliable labeled dataset. On the contrary, we apply novel semi-supervised techniques to estimate soft labels (targets) of the vast amount of existing unlabeled data and then we utilize these soft estimates to improve model training. Overall, four SSL schemes are employed, the Anchor Graph, the Safe Semi-Supervised Regression (SAFER), the Squared-loss Mutual Information Regularization (SMIR), and an equal importance Weighted Average of them (WeiAve). To retain only the most meaning information of the input data, labeled and unlabeled ones, we also employ a Stack Autoencoder (SAE) trained under an unsupervised manner. This way, we handle noise in the input signals, attributed to dimensionality redundancy, without sacrificing meaningful information. Experimental results on the benchmarked dataset of Vaihingen city in Germany indicate that our approach outperforms all state-of-the-art methods in the field using the same type of color orthoimages, though the fact that a limited dataset is utilized (10 times less data or better, compared to other approaches), while our performance is close to the one achieved by high expensive and much more precise input information like the one derived from Light Detection and Ranging (LiDAR) sensors. In addition, the proposed approach can be easily expanded to handle any number of classes, including buildings, vegetation, and ground.

Deep learning paradigm has been shown to be a very efficient classification framework for many application scenarios, including the analysis of Light Detection and Ranging (LiDAR) data for building detection. In fact, deep learning acts as a set of mathematical transformations, encoding the raw input data into appropriate forms of representations that maximize the classification performance. However, it is clear that mathematical computations alone, even highly nonlinear, are not adequate to model the physical properties of a problem, distinguishing, for example, the building structures from vegetation. In this letter, we address this difficulty by augmenting the raw LiDAR data with features coming from a physical interpretation of the information. Then, we exploit a deep learning paradigm based on a convolutional neural network model to find out the best input representations suitable for the classification. As test sites, three complex urban study areas with various kinds of building structures through the LiDAR data set of Vaihingen, Germany were selected. Our method has been evaluated in the context of “ISPRS Test Project on Urban Classification and 3-D Building Reconstruction.” Comparisons with traditional methods, such as artificial neural networks and support vector machine-based classifiers, indicate the outperformance of the proposed approach in terms of robustness and efficiency.

Automatic extraction of buildings from remote sensing data is an attractive research topic, useful for several applications, such as cadastre and urban planning. This is mainly due to the inherent artifacts of the used data and the differences in viewpoint, surrounding environment, and complex shape and size of the buildings. This paper introduces an efficient deep learning framework based on convolutional neural networks (CNNs) toward building extraction from orthoimages. In contrast to conventional deep approaches in which the raw image data are fed as input to the deep neural network, in this paper the height information is exploited as an additional feature being derived from the application of a dense image matching algorithm. As test sites, several complex urban regions of various types of buildings, pixel resolutions and types of data are used, located in Vaihingen in Germany and in Perissa in Greece. Our method is evaluated using the rates of completeness, correctness, and quality and compared with conventional and other “shallow” learning paradigms such as support vector machines. Experimental results indicate that a combination of raw image data with height information, feeding as input to a deep CNN model, provides potentials in building detection in terms of robustness, flexibility, and efficiency.

Nowadays, the transformations of cities into smart cities is a crucial factor in improving the living conditions of the inhabitants as well as addressing emergency situations under the concept of public safety and property loss. In this context, many sensing systems have been designed and developed that provide fire detection and gas leakage alerts. On the other hand, new technologies such edge computing have gained significant attention in recent years. Moreover, the development of recent intelligent applications in IoT aims to integrate several types of systems with automated next-generation emergency calls in case of a serious accident. Currently, there is a lack of studies that combine all the aforementioned technologies. The proposed smart building sensor system, SB112, combines a small-size multisensor-based (temperature, humidity, smoke, flame, CO, LPG, and CNG) scheme with an open-source edge computing framework and automated Next Generation (NG) 112 emergency call functionality. It involves crucial actors such as IoT devices, a Public Safety Answering Point (PSAP), the middleware of a smart city platform, and relevant operators in an end-to-end manner for real-world scenarios. To verify the utility and functionality of the proposed system, a representative end-to-end experiment was performed, publishing raw measurements from sensors as well as a fire alert in real time and with low latency (average latency of 32 ms) to the middleware of a smart city platform. Once the fire was detected, a fully automatic NG112 emergency call to a PSAP was performed. The proposed methodology highlights the potential of the SΒ112 system for exploitation by decision-makers or city authorities.

The transformation of cities into smart cities is a crucial issue for improving the citizens’ living conditions. One of the major goals of the smart city concept is to modernize the urban management utilizing state of the art technologies. A remarkable proliferation of compute-intensive security and safety applications has been disclosed recent years in smart cities. Such applications generate enormous amounts of data demanding strict latency-aware computational processing capabilities. Recent technologies such edge computing is a viable solution to address the limitations of cloud computing, e.g., for real-time smart city environments. Initially, this paper, discusses the trends, the requirements and the technologies for public safety in smart cities under an edge computing concept. Then, proposes the concept and the development of an edge computing platform, namely Distributed Edge Computing IoT Platform (DECIoT), integrating to a smart building sensing system with an NG112 call functionality and a chemical precursor spectroscopic system.

Ensuring citizens’ safety and security has been identified as the number one priority for city authorities when it comes to the use of smart city technologies. Automatic understanding of the scene, and the associated provision of situational awareness for emergency situations, are able to efficiently contribute to such domains. In this study, a Video Analytics Edge Computing (VAEC) system is presented that performs real-time enhanced situation awareness for person detection in a video surveillance manner that is also able to share geolocated person detection alerts and other accompanied crucial information. The VAEC system adopts state-of-the-art object detection and tracking algorithms, and it is integrated with the proposed Distribute Edge Computing Internet of Things (DECIoT) platform. The aforementioned alerts and information are able to be shared, though the DECIoT, to smart city platforms utilizing proper middleware. To verify the utility and functionality of the VAEC system, extended experiments were performed (i) in several light conditions, (ii) using several camera sensors, and (iii) in several use cases, such as installed in fixed position of a building or mounted to a car. The results highlight the potential of VAEC system to be exploited by decision-makers or city authorities, providing enhanced situational awareness.