Discovery of CWISE J052306.42−015355.4, an Extreme T Subdwarf Candidate

We present the discovery of CWISE J052306.42−015355.4, which was found as a faint, significant proper-motion object (0.″52 ± 0.″08 yr−1) using machine-learning tools on the unWISE re-processing of time series images from the Wide-field Infrared Survey Explorer. Using the CatWISE2020 W1 and W2 magnitudes along with a J-band detection from the VISTA Hemisphere Survey, the location of CWISE J052306.42−015355.4 on the W1 − W2 versus J − W2 diagram best matches that of other known, or suspected, extreme T subdwarfs. As there is currently very little knowledge concerning extreme T subdwarfs we estimate a rough distance of ≤68 pc, which results in a tangential velocity of ≤167 km s−1, both of which are tentative. A measured parallax is greatly needed to test these values. We also estimate a metallicity of −1.5 < [M/H] < −0.5 using theoretical predictions.

Grounding Event of Iceberg D28 and Its Interactions with Seabed Topography

Iceberg D28, a giant tabular iceberg that calved from Amery Ice Shelf in September 2019, grounded off Kemp Coast, East Antarctica, from August to September of 2020. The motion of the iceberg is characterized herein by time-series images captured by synthetic aperture radar (SAR) on Sentinel-1 and the moderate resolution imaging spectroradiometer (MODIS) boarded on Terra from 6 August to 15 September 2020. The thickness of iceberg D28 was estimated by utilizing data from altimeters on Cryosat-2, Sentinel-3, and ICESat-2. By using the iceberg draft and grounding point locations inferred from its motion, the maximum water depths at grounding points were determined, varying from 221.72 ± 21.77 m to 269.42 ± 25.66 m. The largest disagreements in seabed elevation inferred from the grounded iceberg and terrain models from the Bedmap2 and BedMachine datasets were over 570 m and 350 m, respectively, indicating a more complicated submarine topography in the study area than that presented by the existing seabed terrain models. Wind and sea water velocities from reanalysis products imply that the driving force from sea water is a more dominant factor than the wind in propelling iceberg D28 during its grounding, which is consistent with previous findings on iceberg dynamics.

Vision Transformer-Based Tailing Detection in Videos

Tailing is defined as an event where a suspicious person follows someone closely. We define the problem of tailing detection from videos as an anomaly detection problem, where the goal is to find abnormalities in the walking pattern of the pedestrians (victim and follower). We, therefore, propose a modified Time-Series Vision Transformer (TSViT), a method for anomaly detection in video, specifically for tailing detection with a small dataset. We introduce an effective way to train TSViT with a small dataset by regularizing the prediction model. To do so, we first encode the spatial information of the pedestrians into 2D patterns and then pass them as tokens to the TSViT. Through a series of experiments, we show that the tailing detection on a small dataset using TSViT outperforms popular CNN-based architectures, as the CNN architectures tend to overfit with a small dataset of time-series images. We also show that when using time-series images, the performance of CNN-based architecture gradually drops, as the network depth is increased, to increase its capacity. On the other hand, a decreasing number of heads in Vision Transformer architecture shows good performance on time-series images, and the performance is further increased as the input resolution of the images is increased. Experimental results demonstrate that the TSViT performs better than the handcrafted rule-based method and CNN-based method for tailing detection. TSViT can be used in many applications for video anomaly detection, even with a small dataset.

Dynamic monitoring of urban built-up object expansion trajectories in Karachi, Pakistan with time series images and the LandTrendr algorithm

In the complex process of urbanization, retrieving its dynamic expansion trajectories with an efficient method is challenging, especially for urban regions that are not clearly distinguished from the surroundings in arid regions. In this study, we propose a framework for extracting spatiotemporal change information on urban disturbances. First, the urban built-up object areas in 2000 and 2020 were obtained using object-oriented segmentation method. Second, we applied LandTrendr (LT) algorithm and multiple bands/indices to extract annual spatiotemporal information. This process was implemented effectively with the support of the cloud computing platform of Earth Observation big data. The overall accuracy of time information extraction, the kappa coefficient, and average detection error were 83.76%, 0.79, and 0.57 a, respectively. These results show that Karachi expanded continuously during 2000–2020, with an average annual growth rate of 4.7%. However, this expansion was not spatiotemporally balanced. The coastal area developed quickly within a shorter duration, whereas the main newly added urban regions locate in the northern and eastern inland areas. This study demonstrated an effective framework for extract the dynamic spatiotemporal change information of urban built-up objects and substantially eliminate the salt-and-pepper effect based on pixel detection. Methods used in our study are of general promotion significance in the monitoring of other disturbances caused by natural or human activities.

Gaussian mixture model decomposition of multivariate signals

We propose a greedy variational method for decomposing a non-negative multivariate signal as a weighted sum of Gaussians, which, borrowing the terminology from statistics, we refer to as a Gaussian mixture model. Notably, our method has the following features: (1) It accepts multivariate signals, i.e., sampled multivariate functions, histograms, time series, images, etc., as input. (2) The method can handle general (i.e., ellipsoidal) Gaussians. (3) No prior assumption on the number of mixture components is needed. To the best of our knowledge, no previous method for Gaussian mixture model decomposition simultaneously enjoys all these features. We also prove an upper bound, which cannot be improved by a global constant, for the distance from any mode of a Gaussian mixture model to the set of corresponding means. For mixtures of spherical Gaussians with common variance $$\sigma ^2$$ σ 2 , the bound takes the simple form $$\sqrt{n}\sigma $$ n σ . We evaluate our method on one- and two-dimensional signals. Finally, we discuss the relation between clustering and signal decomposition, and compare our method to the baseline expectation maximization algorithm.

Central Object Segmentation by Deep Learning to Continuously Monitor Fruit Growth through RGB Images

Monitoring fruit growth is useful when estimating final yields in advance and predicting optimum harvest times. However, observing fruit all day at the farm via RGB images is not an easy task because the light conditions are constantly changing. In this paper, we present CROP (Central Roundish Object Painter). The method involves image segmentation by deep learning, and the architecture of the neural network is a deeper version of U-Net. CROP identifies different types of central roundish fruit in an RGB image in varied light conditions, and creates a corresponding mask. Counting the mask pixels gives the relative two-dimensional size of the fruit, and in this way, time-series images may provide a non-contact means of automatically monitoring fruit growth. Although our measurement unit is different from the traditional one (length), we believe that shape identification potentially provides more information. Interestingly, CROP can have a more general use, working even for some other roundish objects. For this reason, we hope that CROP and our methodology yield big data to promote scientific advancements in horticultural science and other fields.

Remote Sensing of Local Warming Trend in Alberta, Canada during 2001–2020, and Its Relationship with Large-Scale Atmospheric Circulations

Here, the objective was to study the local warming trend and its driving factors in the natural subregions of Alberta using a remote-sensing approach. We applied the Mann–Kendall test and Sen’s slope estimator on the day and nighttime MODIS LST time-series images to map and quantify the extent and magnitude of monthly and annual warming trends in the 21 natural subregions of Alberta. We also performed a correlation analysis of LST anomalies (both day and nighttime) of the subregions with the anomalies of the teleconnection patterns, i.e., Pacific North American (PNA), Pacific decadal oscillation (PDO), Arctic oscillation (AO), and sea surface temperature (SST, Niño 3.4 region) indices, to identify the relationship. May was the month that showed the most significant warming trends for both day and night during 2001–2020 in most of the subregions in the Rocky Mountains and Boreal Forest. Subregions of Grassland and Parkland in southern and southeastern parts of Alberta showed trends of cooling during daytime in July and August and a small magnitude of warming in June and August at night. We also found a significant cooling trend in November for both day and night. We identified from the correlation analysis that the PNA pattern had the most influence in the subregions during February to April and October to December for 2001–2020; however, none of the atmospheric oscillations showed any significant relationship with the significant warming/cooling months.

Influence of Surface Water on Desert Vegetation Expansion at the Landscape Scale: A Case Study of the Daliyabuyi Oasis, Taklamakan Desert

Surface water is an important factor affecting vegetation change in desert areas. However, little research has been conducted on the effects of surface water on vegetation expansion. In this study, the annual spatial distribution range of vegetation and surface water in the Daliyabuyi Oasis from 1990 to 2020 was extracted using Landsat time-series images. Based on multi-temporal and multi-scale remote sensing images, several plots were selected to demonstrate the process of landform change and vegetation expansion, and the influence of surface water on vegetation expansion was analyzed. The results show that the vegetation distribution and surface water coverage have increased from 1990 to 2020; and surface water is a critical factor that drives the expansion of vegetation. On the one hand, surface water in the study area was essential for reshaping the riparian landform, driving the transformation of dunes into floodplains, and increasing the potential colonization sites for vegetation. However, landform changes ultimately changed the redistribution of surface water, ensuring that enough water and nutrients provided by sediment were available for plant growth. Our study provides a critical reference for the restoration of desert vegetation and the sustainable development of oases.