Detecting Nighttime Anomalies from NASA Black Marble Using a Generalized Spatio-Temporally Robust Framework of Machine Leaning Ensembles

Srija Chakraborty

cs.LGOct 5, 2026

Detecting Nighttime Anomalies from NASA Black Marble Using a Generalized Spatio-Temporally Robust Framework of Machine Leaning Ensembles

Authors: Srija Chakraborty

Organizations: Universities Space Research Association

Abstract

Nighttime lights from NASA's Black Marble product suite capture thermal and light emission signals from anomalous events including fires, volcanic eruptions, and gas flaring. Existing detection approaches rely primarily on thermal bands, limiting sensitivity to weaker signals. We propose a novel machine learning framework that jointly models Black Marble M-band and Day/Night Band (DNB) signals to derive a generalized, spatio-temporally robust ensemble of anomaly detectors. The framework iteratively builds detectors that scale across regions, seasons, anomaly classes, and extends over land and ocean. Detection sets at varying confidence levels are derived based on relevant bands and detector agreement. The approach improves true detection rate while reducing spurious detections and results demonstrate strong generalizability with applications in natural hazard monitoring and energy extraction.

Figures & tables

Fig. 1: Proposed two-step iterative anomaly detection approach consisting of anomaly detection, iterative learning with contrastive learning and baseline model ensembles (self-generated labels from detection) for generalization.

Fig. 2: Proposed step 1 consisting of unsupervised background modeling for anomaly detection to extract labels.

Fig. 3: Proposed step 2 to distinguish anomaly and background using a suite of machine learning and baseline ensembles by leveraging spectral dissimilarity.

Fig. 4: Proposed regions of study and corresponding Black Marble tiles. Each tile is centered around a region of study where anomaly is prevalent.

Fig. 5: Anomalous light emission detection from step 1 (DNB-only set) in h08v06 against a regional Landsat composite and (b) and (c) examples of overlap with visible flaring signature (well pads). The high colocation with flare-pads indicates that this set consists of probable flare signals missed by thermal bands and are minimally contaminated from non-flaring sources.

Detectors	Overlap o %
Detectors	h05v05	h08v06	h20v10
Combined-AE	92.66	85.75	76.83
Combined-VAE	92.87	85.69	76.83
DNB	77.21	83.46	67.58
Joint-AE	93.84	96.15	84.83
Joint-VAE	93.84	96.21	84.83

TABLE I: Overlap of Detected Anomalies with VIIRS Nightfire

Tile	overlap $o$					probability of extra detections $p$
Tile	o-cont	o-trip	o-fcnn	o-top10	o-t10_l	p-cont	p-trip	p-fcnn	p-top10	p-t10_l
h05v05	0.9998	0.7097	0.7252	0.9922	0.6495	0.3686	0.2092	0.1060	0.4663	0.0311
	$\pm$ 0.0001	$\pm$ 0.0427	$\pm$ 0.0366	$\pm$ 0.0026	$\pm$ 0.0317	$\pm$ 0.0278	$\pm$ 0.0452	$\pm$ 0.0285	$\pm$ 0.0278	$\pm$ 0.0128
h08v06	1.0000	0.3247	0.9257	0.9621	0.9466	0.6381	0.0000	0.5231	0.3485	0.0057
	$\pm$ 0.0000	$\pm$ 0.0125	$\pm$ 0.0058	$\pm$ 0.0040	$\pm$ 0.0052	$\pm$ 0.0082	$\pm$ 0.0000	$\pm$ 0.0089	$\pm$ 0.0120	$\pm$ 0.0019
h08v06_w	1.0000	0.3724	0.9101	0.9650	0.9196	0.4924	0.0270	0.3570	0.4192	0.0322

TABLE II: Generalized or joint model detection performance across tiles recording overlap (o) and probability of extra detections (p) across all five models. These use all seven Black Marble bands. Bottom three rows show performance on unseen regions. Blue indicates highest performance, magenta shows second highest.

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Have an LLM Write Your Anomaly Detector: Autonomous Discovery of Compact, Interpretable Detectors for Time Series

David Berghaus

Lamarr Institute Germany · Fraunhofer IAIS Germany

Time-Series Anomaly Detection Unsupervised Detection

Open paper AskPDF arXiv

Time-series anomaly detection trades off predictive accuracy, computational efficiency, and interpretability. We use a large language model not as the detector but as the author of one: an autonomous research loop in which the model repeatedly edits a single short NumPy program under a leakage-free objective, keeping the best-scoring detector it finds. The loop discovers two compact detectors, one for univariate and one for multivariate series, that describe short windows by their local spectral features and compare them with the training-region distribution through a covariance-aware distance. On the TSB-AD benchmark these detectors lead the field across metrics, ahead of the strongest classical, deep, and foundation-model baselines including Time-RCD, yet they train no network and use no GPU, and the multivariate detector is faster than every similarly performing baseline. LLM-driven program search is thus a practical route to accurate, efficient, and transparent detectors.

Abstract

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