Remote sensing of recent changes in permafrost-influenced wetlands

Changes in thermokarst lakes have been identified across many high-latitude ecosystems at different spatial and temporal scales. With the declassification of the Landsat archive in 2008, it is now possible to conduct a near-complete yearly time series of homogenous geospatial imagery to assess the trends seen in thermokarst lake surface area. By implementing an automated land- cover classification algorithm, this study examined the dynamics of lake surface area over different spatial and temporal scales in the Tuktoyaktuk Peninsula, Northwest Territories, Canada. Due to the presence of a statistically significant structural temporal break between the years 1997 and 1998, lake areal trends were estimated at two temporal scales, a longer term scale (1985 to 2011) and its component shorter scales (1985 to 1997 and 1998 to 2011). Large lakes saw the greatest changes both in lake areal increase and decreases at all temporal scales, and was suggested to drive the overall changes in surface area. In addition, regional differences were observed in the spatial distribution of individual lake area trends. On the broad scale, a latitudinal divide bifurcated the peninsula into two regions of approximately equal area, where the northern region exhibited general trends of lake areal decline, while the southern region exhibited general trends of lake areal increase. Within these regions, meso- and local hot and cold spots were identified, some that exhibited trends in concordance with local surroundings, while others represented local spatial heterogeneity in areal trends. The spatiotemporal trends in lake area were suggested to be influenced at varying scales by atmospheric and climate variables, and by ground characteristics such as coincident permafrost and surficial geology. As the Arctic continues to warm, a continued observation of thermokarst lake evolution over both broad landscapes and localised regions will be increasingly valuable to future studies that investigate the resulting transformation of the Arctic permafrost region

Sky and ground segmentation in the navigation visions of the planetary rovers

Sky and ground are two essential semantic components in computer vision, robotics, and remote sensing. The sky and ground segmentation has become increasingly popular. This research proposes a sky and ground segmentation framework for the rover navigation visions by adopting weak supervision and transfer learning technologies. A new sky and ground segmentation neural network (network in U-shaped network (NI-U-Net)) and a conservative annotation method have been proposed. The pre-trained process achieves the best results on a popular open benchmark (the Skyfinder dataset) by evaluating seven metrics compared to the state-of-the-art. These seven metrics achieve 99.232%, 99.211%, 99.221%, 99.104%, 0.0077, 0.0427, and 98.223% on accuracy, precision, recall, dice score (F1), misclassification rate (MCR), root mean squared error (RMSE), and intersection over union (IoU), respectively. The conservative annotation method achieves superior performance with limited manual intervention. The NI-U-Net can operate with 40 frames per second (FPS) to maintain the real-time property. The proposed framework successfully fills the gap between the laboratory results (with rich idea data) and the practical application (in the wild). The achievement can provide essential semantic information (sky and ground) for the rover navigation vision

Identity Construction in a Misogynist Incels Forum

Online communities of involuntary celibates (incels) are a prominent source of misogynist hate speech. In this paper, we use quantitative text and network analysis approaches to examine how identity groups are discussed on incels.is, the largest black-pilled incels forum. We find that this community produces a wide range of novel identity terms and, while terms for women are most common, mentions of other minoritized identities are increasing. An analysis of the associations made with identity groups suggests an essentialist ideology where physical appearance, as well as gender and racial hierarchies, determine human value. We discuss implications for research into automated misogynist hate speech detection.Comment: Workshop on Online Abuse and Harms (WOAH) 202

Investigation of environmental indices from the Earth Resources Technology Satellite

The author has identified the following significant results. Land use change, water quality, and air quality indices have been calculated from analysis of ERTS-1 multispectral scanning imagery and computer compatible tapes. Specifications have been developed and discussed for an ERTS-1 environmental monitoring system which help to serve the information needs of environmental managers at the Federal, state, regional, and local level. General conclusions of the investigation are that ERTS-1 data is very useful in land use mapping and updating to 10-15 categories, and can provide an overall measure of air and water turbidity; however, more and better ground truth and possibly additional spacecraft sensors will be required if specific air and water pollutants are to be quantified from satellite data

Aesthetically Relevant Image Captioning

Image aesthetic quality assessment (AQA) aims to assign numerical aesthetic ratings to images whilst image aesthetic captioning (IAC) aims to generate textual descriptions of the aesthetic aspects of images. In this paper, we study image AQA and IAC together and present a new IAC method termed Aesthetically Relevant Image Captioning (ARIC). Based on the observation that most textual comments of an image are about objects and their interactions rather than aspects of aesthetics, we first introduce the concept of Aesthetic Relevance Score (ARS) of a sentence and have developed a model to automatically label a sentence with its ARS. We then use the ARS to design the ARIC model which includes an ARS weighted IAC loss function and an ARS based diverse aesthetic caption selector (DACS). We present extensive experimental results to show the soundness of the ARS concept and the effectiveness of the ARIC model by demonstrating that texts with higher ARS's can predict the aesthetic ratings more accurately and that the new ARIC model can generate more accurate, aesthetically more relevant and more diverse image captions. Furthermore, a large new research database containing 510K images with over 5 million comments and 350K aesthetic scores, and code for implementing ARIC are available at https://github.com/PengZai/ARIC.Comment: Aceepted by AAAI2023. Code and results available at https://github.com/PengZai/ARI

Development and verification of the NASA Multi-Angle Imager for Aerosols operational cloud mask

The National Aeronautics and Space Administration (NASA) Multi-Angle Imager for Aerosols (MAIA) instrument is set to launch in 2022 with the mission of quantifying the epidemiological relationships between aerosols and human health. The MAIA instrument's primary product is a level 2 aerosol particulate matter concentration measurement collected over cloud-free pixels. The quality of this product heavily depends on the validity of the cloud mask. In this project, we present a cloud masking algorithm for MAIA constrained to its hardware. It consists of 7 observables that are tested against predetermined static thresholds. Both observables and thresholds are a function of scene type, which is a unique combination of sun-view geometry, day of year and surface type, including a novel surface classification scheme derived from the Multi-Angle Implementation of Atmospheric Correction Bi-Directional Reflectance Distribution Function (MAIAC BRDF) data set. The cloud mask algorithm works by checking if an observation exceeds or falls short of a threshold for any of the 7 observables, resulting in a cloudy or clear classification. The thresholds are derived to match the performance of the Terra Moderate Resolution Imaging Spectro-Radiometer (MODIS) high-confidence-cloud cloud mask to achieve cloud conservative behavior. The algorithm allows tuning of the conservativeness by introducing the quantities of Distance-to-Threshold, Activation Value and number of tests to activate. These user-specified parameters determine how much confidence is needed for a cloudy or clear classification. The results are presented for the Los Angeles primary target area. The overall agreement between the MODIS cloud mask and the MAIA cloud mask (MCM) is 92.9%. Of the 7.1% disagreement, 60% of it was due to false positives by the MCM, considering MODIS as the truth. The MCM is more than 90% in agreement with MODIS for deep non-sun-glint water and the first 11 of the 16 snow-free land surface types. It differs from the MODIS cloud mask the most over bright desert, mountains and coastlines due to false cloudy flags. It agrees well with the MODIS cloud mask for cumulus, stratus and high cirrus, with greater disagreements over cloud edges, smoke plumes from wildfires, and very thin cirrus. The MCM agrees well with the MODIS cloud mask (>85%) for most solar zenith angles between 25 and 53 degrees, viewing zenith angles less than 60 degrees, and relative azimuth angles between 105 and 135 degrees. Several recommendations for improving the MCM are discussed, and its advantages over the MODIS cloud mask

Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models

To recognize and mitigate harms from large language models (LLMs), we need to understand the prevalence and nuances of stereotypes in LLM outputs. Toward this end, we present Marked Personas, a prompt-based method to measure stereotypes in LLMs for intersectional demographic groups without any lexicon or data labeling. Grounded in the sociolinguistic concept of markedness (which characterizes explicitly linguistically marked categories versus unmarked defaults), our proposed method is twofold: 1) prompting an LLM to generate personas, i.e., natural language descriptions, of the target demographic group alongside personas of unmarked, default groups; 2) identifying the words that significantly distinguish personas of the target group from corresponding unmarked ones. We find that the portrayals generated by GPT-3.5 and GPT-4 contain higher rates of racial stereotypes than human-written portrayals using the same prompts. The words distinguishing personas of marked (non-white, non-male) groups reflect patterns of othering and exoticizing these demographics. An intersectional lens further reveals tropes that dominate portrayals of marginalized groups, such as tropicalism and the hypersexualization of minoritized women. These representational harms have concerning implications for downstream applications like story generation.Comment: To appear at ACL 2023, 9 pages, 3 figures, 3 table

Data mining using concepts of independence, unimodality and homophily

With the widespread use of information technologies, more and more complex data is generated and collected every day. Such complex data is various in structure, size, type and format, e.g. time series, texts, images, videos and graphs. Complex data is often high-dimensional and heterogeneous, which makes the separation of the wheat (knowledge) from the chaff (noise) more difficult. Clustering is a main mode of knowledge discovery from complex data, which groups objects in such a way that intra-group objects are more similar than inter-group objects. Traditional clustering methods such as k-means, Expectation-Maximization clustering (EM), DBSCAN and spectral clustering are either deceived by "the curse of dimensionality" or spoiled by heterogenous information. So, how to effectively explore complex data? In some cases, people may only have some partial information about the complex data. For example, in social networks, not every user provides his/her profile information such as the personal interests. Can we leverage the limited user information and friendship network wisely to infer the likely labels of the unlabeled users so that the advertisers can do accurate advertising? This is the problem of learning from labeled and unlabeled data, which is literarily attributed to semi-supervised classification. To gain insights into these problems, this thesis focuses on developing clustering and semi-supervised classification methods that are driven by the concepts of independence, unimodality and homophily. The proposed methods leverage techniques from diverse areas, such as statistics, information theory, graph theory, signal processing, optimization and machine learning. Specifically, this thesis develops four methods, i.e. FUSE, ISAAC, UNCut, and wvGN. FUSE and ISAAC are clustering techniques to discover statistically independent patterns from high-dimensional numerical data. UNCut is a clustering technique to discover unimodal clusters in attributed graphs in which not all the attributes are relevant to the graph structure. wvGN is a semi-supervised classification technique using the theory of homophily to infer the labels of the unlabeled vertices in graphs. We have verified our clustering and semi-supervised classification methods on various synthetic and real-world data sets. The results are superior to those of the state-of-the-art.Täglich werden durch den weit verbreiteten Einsatz von Informationstechnologien mehr und mehr komplexe Daten generiert und gesammelt. Diese komplexen Daten unterscheiden sich in der Struktur, Größe, Art und Format. Häufig anzutreffen sind beispielsweise Zeitreihen, Texte, Bilder, Videos und Graphen. Dabei sind diese Daten meist hochdimensional und heterogen, was die Trennung des Weizens ( Wissen ) von der Spreu ( Rauschen ) erschwert. Die Cluster Analyse ist dabei eine der wichtigsten Methoden um aus komplexen Daten wssen zu extrahieren. Dabei werden die Objekte eines Datensatzes in einer solchen Weise gruppiert, dass intra-gruppierte Objekte ähnlicher sind als Objekte anderer Gruppen. Der Einsatz von traditionellen Clustering-Methoden wie k-Means, Expectation-Maximization (EM), DBSCAN und Spektralclustering wird dabei entweder "durch der Fluch der Dimensionalität" erschwert oder ist angesichts der heterogenen Information nicht möglich. Wie erforscht man also solch komplexe Daten effektiv? Darüber hinaus ist es oft der Fall, dass für Objekte solcher Datensätze nur partiell Informationen vorliegen. So gibt in sozialen Netzwerken nicht jeder Benutzer seine Profil-Informationen wie die persönlichen Interessen frei. Können wir diese eingeschränkten Benutzerinformation trotzdem in Kombination mit dem Freundschaftsnetzwerk nutzen, um von von wenigen, einer Klasse zugeordneten Nutzern auf die anderen zu schließen. Beispielsweise um zielgerichtete Werbung zu schalten? Dieses Problem des Lernens aus klassifizierten und nicht klassifizierten Daten wird dem semi-supversised Learning zugeordnet. Um Einblicke in diese Probleme zu gewinnen, konzentriert sich diese Arbeit auf die Entwicklung von Clustering- und semi-überwachten Klassifikationsmethoden, die von den Konzepten der Unabhängigkeit, Unimodalität und Homophilie angetrieben werden. Die vorgeschlagenen Methoden nutzen Techniken aus verschiedenen Bereichen der Statistik, Informationstheorie, Graphentheorie, Signalverarbeitung, Optimierung und des maschinelles Lernen. Dabei stellt diese Arbeit vier Techniken vor: FUSE, ISAAC, UNCut, sowie wvGN. FUSE und ISAAC sind Clustering-Techniken, um statistisch unabhängige Muster aus hochdimensionalen numerischen Daten zu entdecken. UNCut ist eine Clustering-Technik, um unimodale Cluster in attributierten Graphen zu entdecken, in denen die Kanten und Attribute heterogene Informationen liefern. wvGN ist eine halbüberwachte Klassifikationstechnik, die Homophilie verwendet, um von gelabelten Kanten auf ungelabelte Kanten im Graphen zu schließen. Wir haben diese Clustering und semi-überwachten Klassifizierungsmethoden auf verschiedenen synthetischen und realen Datensätze überprüft. Die Ergebnisse sind denen von bisherigen State-of-the-Art-Methoden überlegen