Slums, characterized by sub-standard housing conditions, are a common in fast growing Asian cities. However, reliable and up-to-date information on their locations and development dynamics is scarce. Despite numerous studies, the task of delineating slum areas remains a challenge and no general agreement exists about the most suitable method for detecting or assessing detection performance. In this paper, standard computer vision methods – Bag of Visual Words framework and Speeded-Up Robust Features have been applied for image-based classification of slum and non-slum areas in Kalyan and Bangalore, India, using very high resolution RGB images. To delineate slum areas, image segmentation is performed as pixel-level classification for three classes: Slums, Built-up and Non-Built-up. For each of the three classes, image tiles were randomly selected using ground truth observations. A multi-class support vector machine classifier has been trained on 80% of the tiles and the remaining 20% were used for testing. The final image segmentation has been obtained by classification of every 10th pixel followed by a majority filtering assigning classes to all remaining pixels. The results demonstrate the ability of the method to map slums with very different visual characteristics in two very different Indian cities.

Context.As radio telescopes increase in sensitivity and flexibility, so do their complexity and data rates. For this reason, automated system health management approaches are becoming increasingly critical to ensure nominal telescope operations.Aims.We propose a new machine-learning anomaly detection framework for classifying both commonly occurring anomalies in radio telescopes as well as detecting unknown rare anomalies that the system has potentially not yet seen. To evaluate our method, we present a dataset consisting of 6708 autocorrelation-based spectrograms from the Low Frequency Array (LOFAR) telescope and assign ten different labels relating to the system-wide anomalies from the perspective of telescope operators. This includes electronic failures, miscalibration, solar storms, network and compute hardware errors, among many more.Methods.We demonstrate how a novel self-supervised learning (SSL) paradigm, that utilises both context prediction and reconstruction losses, is effective in learning normal behaviour of the LOFAR telescope. We present the Radio Observatory Anomaly Detector (ROAD), a framework that combines both SSL-based anomaly detection and a supervised classification, thereby enabling both classification of both commonly occurring anomalies and detection of unseen anomalies.Results.We demonstrate that our system works in real time in the context of the LOFAR data processing pipeline, requiring <1ms to process a single spectrogram. Furthermore, ROAD obtains an anomaly detection F-2 score of 0.92 while maintaining a false positive rate of 2%, as well as a mean per-class classification F-2 score of 0.89, outperforming other related works.

The growing demand from science and industry inspired rapid advances in Artificial Intelligence (AI). The increased use of AI and the reliability and trust required by automated decision making and standards of scientific rigour, led to the boom of eXplainable Artificial Intelligence (XAI). DIANNA (Deep Insight And Neural Network Analysis) is the first Python package of systematically selected XAI methods supporting the Open Neural Networks Exchange (ONNX) format. DIANNA is built by and designed for all research software engineers and researchers, also non-AI experts.

We show that using nearest neighbours in the latent space of autoencoders (AE) significantly improves performance of semi-supervised novelty detection in both single and multi-class contexts. Autoencoding methods detect novelty by learning to differentiate between the non-novel training class(es) and all other unseen classes. Our method harnesses a combination of the reconstructions of the nearest neighbours and the latent-neighbour distances of a given input's latent representation. We demonstrate that our nearest-latent-neighbours (NLN) algorithm is memory and time efficient, does not require significant data augmentation, nor is reliant on pre-trained networks. Furthermore, we show that the NLN-algorithm is easily applicable to multiple datasets without modification. Additionally, the proposed algorithm is agnostic to autoencoder architecture and reconstruction error method. We validate our method across several standard datasets for a variety of different autoencoding architectures such as vanilla, adversarial and variational autoencoders using either reconstruction, residual or feature consistent losses. The results show that the NLN algorithm grants up to a 17% increase in Area Under the Receiver Operating Characteristics (AUROC) curve performance for the multi-class case and 8% for single-class novelty detection.