MULTI-MODAL SELF-SUPERVISED REPRESENTATION LEARNING FOR EARTH OBSERVATION

Self-Supervised learning (SSL) has reduced the performance gap between supervised and unsupervised learning, due to its ability to learn invariant representations. This is a boon to the domains like Earth Observation (EO), where labelled data availability is scarce but unlabelled data is freely available. While Transfer Learning from generic RGB pre-trained models is still common-place in EO, we argue that, it is essential to have good EO domain specific pre-trained model in order to use with downstream tasks with limited labelled data. Hence, we explored the applicability of SSL with multi-modal satellite imagery for downstream tasks. For this we utilised the state-of-art SSL architectures i.e. BYOL and SimSiam to train on EO data. Also to obtain better invariant representations, we considered multi-spectral (MS) images and synthetic aperture radar (SAR) images as separate augmented views of an image to maximise their similarity. Our work shows that by learning single channel representations through non-contrastive learning, our approach can outperform ImageNet pre-trained models significantly on a scene classification task. We further explored the usefulness of a momentum encoder by comparing the two architectures i.e. BYOL and SimSiam but did not identify a significant improvement in performance between the models

Self-Supervised Learning for Invariant Representations From Multi-Spectral and SAR Images

Self-Supervised learning (SSL) has become the new state of the art in several domain classification and segmentation tasks. One popular category of SSL are distillation networks such as Bootstrap Your Own Latent (BYOL). This work proposes RS-BYOL, which builds on BYOL in the remote sensing (RS) domain where data are non-trivially different from natural RGB images. Since multi-spectral (MS) and synthetic aperture radar (SAR) sensors provide varied spectral and spatial resolution information, we utilise them as an implicit augmentation to learn invariant feature embeddings. In order to learn RS based invariant features with SSL, we trained RS-BYOL in two ways, i.e. single channel feature learning and three channel feature learning. This work explores the usefulness of single channel feature learning from random 10 MS bands of 10m-20 m resolution and VV-VH of SAR bands compared to the common notion of using three or more bands. In our linear probing evaluation, these single channel features reached a 0.92 F1 score on the EuroSAT classification task and 59.6 mIoU on the IEEE Data Fusion Contest (DFC) segmentation task for certain single bands. We also compare our results with ImageNet weights and show that the RS based SSL model outperforms the supervised ImageNet based model. We further explore the usefulness of multi-modal data compared to single modality data, and it is shown that utilising MS and SAR data allows better invariant representations to be learnt than utilising only MS data

Comparative Study of Feature Representations for Disaster Tweet Classification

Twitter is a popular social media platform where users publicly broadcast short messages on a myriad of topics. In recent years it has enjoyed an increased usage around disaster events due to availability of information in near real time. Additionally, enhanced information representations to facilitate the classification of social media in terms of relevancy and type of information is currently a highly active research area (Ashktorab et al., 2014, Imran et al., 2014, Win et al., 2018). In this work we consider the usefulness and reliability of a range of representation models in the analysis of disaster related social media

MediaEval2019: Flood Detection in Time Sequence Satellite Images

In this work, we present a flood detection technique from time series satellite images for the City-centered satellite sequences (CCSS) task in the MediaEval 2019 competition [1]. This work utilises a three channel feature indexing technique [13] along with a VGG16 pretrained model for automatic detection of floods. We also compared our result with RGB images and a modified NDWI technique by Mishra et al, 2015 [15]. The result shows that the three channel feature indexing technique performed the best with VGG16 and is a promising approach to detect floods from time series satellite images

Transport of Mars-Crossing Asteroids from the Quasi-Hilda Region

We employ set oriented methods in combination with graph partitioning algorithms to identify key dynamical regions in the Sun-Jupiter-particle three-body system. Transport rates from a region near the 3:2 Hilda resonance into the realm of orbits crossing Mars' orbit are computed. In contrast to common numerical approaches, our technique does not depend on single long term simulations of the underlying model. Thus, our statistical results are particularly reliable since they are not affected by a dynamical behavior which is almost nonergodic (i.e., dominated by strongly almost invariant sets)

Effectiveness of Advanced Nitrogen-Removal Onsite Wastewater Treatment Systems in a New England Coastal Community

Wastewater is a major source of nitrogen (N) to groundwater and coastal waterbodies, threatening both environmental and public health. Advanced N-removal onsite wastewater treatment systems (OWTS) are used to reduce effluent N concentration; however, few studies have assessed their effectiveness. We evaluated the total N (TN) concentration of effluent from 50 advanced N-removal OWTS in Charlestown, Rhode Island, USA for 3 years. We quantified differences in effectiveness as a function of N-removal technology and home occupancy pattern (seasonal vs. year-round use), and examined the relationship between wastewater properties and TN concentration. RX30 systems produced the lowest median TN concentration (mg N/L) (13.2), followed by FAST (13.4), AX20 (14.9), and Norweco (33.8). Compliance with the state’s regulatory standard for effluent TN concentration (19 mg N/L) was highest for RX30 systems (78%), followed by AX20 (73%), FAST (67%), and Norweco (0%). Occupancy pattern did not affect effluent TN concentration. Variation in TN concentration was driven by ammonium and nitrate for all technologies, and also by temperature for FAST and pH for Norweco. Median daily (g N/day) and annual (kg N/yr) N loads were significantly higher for year-round (5.3 and 2.3) than for seasonal (3.7 and 0.41) systems, likely due to differences in volume of wastewater treated. Our results suggest that advanced N-removal OWTS vary in their compliance with the state regulatory standard for effluent TN and can withstand long periods of non-use without compromising effectiveness. Nevertheless, systems used year-round do produce a higher daily and annual N load than seasonally-used systems

Greenhouse Gas Emissions from Advanced Nitrogen-Removal Onsite Wastewater Treatment Systems

Advanced onsite wastewater treatment systems (OWTS) designed to remove nitrogen from residential wastewater play an important role in protecting environmental and public health. Nevertheless, the microbial processes involved in treatment produce greenhouse gases (GHGs) that contribute to global climate change, including CO2, CH4, N2O. We measured GHG emissions from 27 advanced N-removal OWTS in the towns of Jamestown and Charlestown, Rhode Island, USA, and assessed differences in flux based on OWTS technology, home occupancy (year-round vs. seasonal), and zone within the system (oxic vs. anoxic/hypoxic). We also investigated the relationship between flux and wastewater properties. Flux values for CO2, CH4, and N2O ranged from −0.44 to 61.8, −0.0029 to 25.3, and −0.02 to 0.23 μmol GHG m−2 s−1, respectively. CO2 and N2O flux varied among technologies, whereas occupancy pattern did not significantly impact any GHG fluxes. CO2 and CH4 – but not N2O – flux was significantly higher in the anoxic/hypoxic zone than in the oxic zone. Greenhouse gas fluxes in the oxic zone were not related to any wastewater properties. CO2 and CH4 flux from the anoxic/hypoxic zone peaked at ~22-23 °C, and was negatively correlated with dissolved oxygen levels, the latter suggesting that CO2 and CH4 flux result primarily from anaerobic respiration. Ammonium concentration and CH4 flux were positively correlated, likely due to inhibition of CH4 oxidation by NH4+. N2O flux in the anoxic/hypoxic zone was not correlated to any wastewater property. We estimate that advanced N-removal OWTS contribute 262 g CO2 equivalents capita−1 day−1, slightly lower than emissions from conventional OWTS. Our results suggest that technology influences CO2 and N2O flux and zone influences CO2 and CH4 flux, while occupancy pattern does not appear to impact GHG flux. Manipulating wastewater properties, such as temperature and dissolved oxygen, may help mitigate GHG emissions from these systems

Influence of Season, Occupancy Pattern, and Technology on Structure and Composition of Nitrifying and Denitrifying Bacterial Communities in Advanced Nitrogen-Removal Onsite Wastewater Treatment Systems

Advanced onsite wastewater treatment systems (OWTS) use biological nitrogen removal (BNR) to mitigate the threat that N-rich wastewater poses to coastal waterbodies and groundwater. These systems lower the N concentration of effluent via sequential microbial nitrification and denitrification. We used high-throughput sequencing to evaluate the structure and composition of nitrifying and denitrifying bacterial communities in advanced N-removal OWTS, targeting the genes encoding ammonia monooxygenase (amoA) and nitrous oxide reductase (nosZ) present in effluent from 44 advanced systems. We used QIIME2 and the phyloseq package in R to examine differences in taxonomy and alpha and beta diversity as a function of advanced OWTS technology, occupancy pattern (seasonal vs. year-round use), and season (June vs. September). Richness and Shannon’s diversity index for amoA were significantly influenced by season, whereas technology influenced nosZ diversity significantly. Season also had a strong influence on differences in beta diversity among amoA communities, and had less influence on nosZ communities, whereas technology had a stronger influence on nosZ communities. Nitrosospira and Nitrosomonas were the main genera of nitrifiers in advanced N-removal OWTS, and the predominant genera of denitrifiers included Zoogloea, Thauera, and Acidovorax. Differences in taxonomy for each gene generally mirrored those observed in diversity patterns, highlighting the possible importance of season and technology in shaping communities of amoA and nosZ, respectively. Knowledge gained from this study may be useful in understanding the connections between microbial communities and OWTS performance and may help manage systems in a way that maximizes N removal

Funder open access platforms - a welcome innovation?

Funding organisations commissioning their own open access publishing platforms is a relatively recent development in the OA environment, with the European Commission following the Wellcome Trust and the Gates Foundation in financing such an initiative. But in what ways, for better or worse, do these new platforms disrupt or complement the scholarly communications landscape? Tony Ross-Hellauer, Birgit Schmidt and Bianca Kramer examine the ethical, organisational, and economic strengths and weaknesses of funder OA platforms to scope the opportunities and threats they present in the transition to OA. While they may help to increase OA uptake, control costs, and lower the administrative burden on researchers, possible unintended consequences include conflicts of interest, difficulties of scale, or potential vendor lock-in