Related papers: On-Orbit Real-Time Wildfire Detection Under On-Boa…
Optical emission line diagnostics, which are a common tool to constrain the properties of the interstellar medium (ISM) of galaxies, become progressively inaccessible at higher redshifts for ground-based facilities. Far-infrared (FIR)…
Deep learning techniques have greatly enhanced the performance of fire detection in videos. However, video-based fire detection models heavily rely on labeled data, and the process of data labeling is particularly costly and time-consuming,…
In this paper, we present a high-performance and light-weight deep learning model for Remote Sensing Image Classification (RSIC), the task of identifying the aerial scene of a remote sensing image. To this end, we first valuate various…
Photothermal microscopy is an emerging tool for measuring light-matter interactions with single-molecule sensitivity. It is generally believed that the spectral acquisition speed in photothermal microscopy is limited by the slow thermal…
We present the results of a search of infrared excess candidates in a comprehensive (29\,000 stars) magnitude limited sample of dwarf stars, spanning the spectral range F2-K0, and brighter than V$=$15 mag. We searched the sample within the…
WINERED is a newly built high-efficiency (throughput$ > 25-30\%$) and high-resolution spectrograph customized for short NIR bands at 0.9-1.35 ${\rm \mu}$m. WINERED is equipped with ambient temperature optics and a cryogenic camera using a…
The highest accuracy object detectors to date are based either on a two-stage approach such as Fast R-CNN or one-stage detectors such as Retina-Net or SSD with deep and complex backbones. In this paper we present TigerNet - simple yet…
Methane is a potent greenhouse gas, and detecting its leaks early via hyperspectral satellite imagery can help mitigate climate change. Meanwhile, many existing missions operate in manual tasking regimes only, thus missing potential events…
While radial velocity surveys have demonstrated that the population of gas giants peaks around $3~\text{au}$, the most recent high-contrast imaging surveys have only been sensitive to planets beyond $\sim~10~\text{au}$. Sensitivity at small…
Massive multiple input and multiple output (MIMO) systems with orthogonal frequency division multiplexing (OFDM) are foundational for downlink multi-user (MU) communication in future wireless networks, for their ability to enhance spectral…
A new detection system for X-/Gamma-ray broad energy passband detectors for astronomy has been developed. This system is based on Silicon Drift Detectors (SDDs) coupled with scintillator bars; the SDDs act as a direct detector of soft (<30…
Precise near-infrared radial velocimetry enables efficient detection and transit verification of low-mass extrasolar planets orbiting M dwarf hosts, which are faint for visible-wavelength radial velocity surveys. The TripleSpec Exoplanet…
The increasing accessibility of radiometric thermal imaging sensors for unmanned aerial vehicles (UAVs) offers significant potential for advancing AI-driven aerial wildfire management. Radiometric imaging provides per-pixel temperature…
The detection of early signs of volcanic unrest preceding an eruption, in the form of ground deformation in Interferometric Synthetic Aperture Radar (InSAR) data is critical for assessing volcanic hazard. In this work we treat this as a…
Wildfire forecasting is of paramount importance for disaster risk reduction and environmental sustainability. We approach daily fire danger prediction as a machine learning task, using historical Earth observation data from the last decade…
Accurate forecasts of fine particulate matter (PM 2.5) from wildfire smoke are crucial to safeguarding cardiopulmonary public health. Existing forecasting systems are trained on sparse and inaccurate ground truths, and do not take…
Accurate prediction of next-day wildfire spread is critical for disaster response and resource allocation. Existing deep learning approaches typically concatenate heterogeneous geospatial inputs into a single tensor, ignoring the…
We present the implementation of four FPGA-accelerated convolutional neural network (CNN) models for onboard cloud detection in resource-constrained CubeSat missions, leveraging Xilinx's Vitis AI (VAI) framework and Deep Learning Processing…
Detection and classification of vehicles are very significant components in an Intelligent-Transportation System. Existing solutions not only use heavy-weight and costly equipment, but also largely depend on constant cloud (Internet)…
Autonomous satellite servicing missions must execute close-range rendezvous under stringent safety and operational constraints while remaining computationally tractable for onboard use and robust to uncertainty in sensing, actuation, and…