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The Netherlands police are looking for measures to examine sentiment on social media related to protest demonstrations. While models exist to detect more subtle expressions of sentiment within tweets, models trained in the Dutch language are scarce. Being able to predict sentiment development during protests is relevant for parties like the Dutch government and the police to get more insight to when and where potential law enforcement is needed for public order and safety. Therefore, to analyse sentiment before, during, and after protest demonstrations, data was collected with tweets related to a Black Lives Matter protest that took place in Amsterdam during the COVID-19 pandemic. All tweets have been manually labelled by a dedicated open-source intelligence (OSINT) team within the Netherlands police following an established protocol. Both the data and the protocol are available, and interesting for researchers in natural language processing, topic detection, sentiment analysis, and protests analysis. The developed labelling tool for the labelling process is publicly available.

The Sentinel‐1 mission is part of the European Copernicus program aiming at providing observations for Land, Marine and Atmosphere Monitoring, Emergency Management, Security and Climate Change. It is a constellation of two (Sentinel‐1 A and B) Synthetic Aperture Radar (SAR) satellites. The SAR wave mode (WV) routinely collects high‐resolution SAR images of the ocean surface during day and night and through clouds. In this study, a subset of more than 37,000 SAR images is labelled corresponding to ten geophysical phenomena, including both oceanic and meteorologic features. These images cover the entire open ocean and are manually selected from Sentinel‐1A WV acquisitions in 2016. For each image, only one prevalent geophysical phenomenon with its prescribed signature and texture is selected for labelling. The SAR images are processed into a quick‐look image provided in the formats of PNG and GeoTIFF as well as the associated labels. They are convenient for both visual inspection and machine learning‐based methods exploitation. The proposed dataset is the first one involving different oceanic or atmospheric phenomena over the open ocean. It seeks to foster the development of strategies or approaches for massive ocean SAR image analysis. A key objective was to allow exploiting the full potential of Sentinel‐1 WV SAR acquisitions, which are about 60,000 images per satellite per month and freely available. Such a dataset may be of value to a wide range of users and communities in deep learning, remote sensing, oceanography and meteorology.

The Sentinel‐1 mission is part of the European Copernicus program aiming at providing observations for Land, Marine and Atmosphere Monitoring, Emergency Management, Security and Climate Change. It is a constellation of two (Sentinel‐1 A and B) Synthetic Aperture Radar (SAR) satellites. The SAR wave mode (WV) routinely collects high‐resolution SAR images of the ocean surface during day and night and through clouds. In this study, a subset of more than 37,000 SAR images is labelled corresponding to ten geophysical phenomena, including both oceanic and meteorologic features. These images cover the entire open ocean and are manually selected from Sentinel‐1A WV acquisitions in 2016. For each image, only one prevalent geophysical phenomenon with its prescribed signature and texture is selected for labelling. The SAR images are processed into a quick‐look image provided in the formats of PNG and GeoTIFF as well as the associated labels. They are convenient for both visual inspection and machine learning‐based methods exploitation. The proposed dataset is the first one involving different oceanic or atmospheric phenomena over the open ocean. It seeks to foster the development of strategies or approaches for massive ocean SAR image analysis. A key objective was to allow exploiting the full potential of Sentinel‐1 WV SAR acquisitions, which are about 60,000 images per satellite per month and freely available. Such a dataset may be of value to a wide range of users and communities in deep learning, remote sensing, oceanography and meteorology.

Road Traffic Noise (RTN) is one of the biggest pollutants in modern cities, which is known to affect public health to be the direct cause of many illnesses for their inhabitants. Until recently, RTN maps have been generated using representative static measurements collected by experts, after manually discarding all non-traffic related noise events, or Anomalous Noise Events (ANEs). However, the automation of noise measurements using Wireless Acoustic Sensor Networks (WASNs) is allowing the development of dynamic maps, which require the detection of non-traffic noise sources in real-time in order to provide accurate noise level measurements. In this work, the manual an automatic removal of ANEs are compared. The latter is based on two versions of the Anomalous Noise Event Detector (ANED) designed to detect ANEs within a WASN in real-time as a two-class classifier. The experiments on 4 h and 44 min of real-life audio data show similar error rates among all the considered annotation methods. However, the detailed analysis of the experiments reveal, on the one hand, inconsistent manual annotations in certain non-ANE labelling situations, where non-coincident expert-based decisions are observed; and, on the other hand, the decrease of the overall accuracy of the ANED-based approaches due to the large number of false alarms in the case of RTN class. Thus, although the results demonstrate the viability of the automated removal of ANEs, further research should be conducted to keep improving the automation of ANEs annotation.

The Sentinel‐1 mission is part of the European Copernicus program aiming at providing observations for Land, Marine and Atmosphere Monitoring, Emergency Management, Security and Climate Change. It is a constellation of two (Sentinel‐1 A and B) Synthetic Aperture Radar (SAR) satellites. The SAR wave mode (WV) routinely collects high‐resolution SAR images of the ocean surface during day and night and through clouds. In this study, a subset of more than 37,000 SAR images is labelled corresponding to ten geophysical phenomena, including both oceanic and meteorologic features. These images cover the entire open ocean and are manually selected from Sentinel‐1A WV acquisitions in 2016. For each image, only one prevalent geophysical phenomenon with its prescribed signature and texture is selected for labelling. The SAR images are processed into a quick‐look image provided in the formats of PNG and GeoTIFF as well as the associated labels. They are convenient for both visual inspection and machine learning‐based methods exploitation. The proposed dataset is the first one involving different oceanic or atmospheric phenomena over the open ocean. It seeks to foster the development of strategies or approaches for massive ocean SAR image analysis. A key objective was to allow exploiting the full potential of Sentinel‐1 WV SAR acquisitions, which are about 60,000 images per satellite per month and freely available. Such a dataset may be of value to a wide range of users and communities in deep learning, remote sensing, oceanography and meteorology.