arXiv subjects

Ebenezer Afrifa-Yamoah

Publications and source records attributed to Ebenezer Afrifa-Yamoah.

2 recordsLinked to original sources

arXiv2026

Super-resolution as Spatial Downscaling: Coherence, Structure and Task Utility in Earth Observation

Learned satellite super-resolution is commonly assessed by pixel fidelity and potential downlink savings, but these criteria do not establish whether the reconstructed field preserves spatial structure or supports reliable downstream analysis. We treat single-image super-resolution as a spatial downscaling problem and evaluate four lightweight networks, including our compact 0.36 million-parameter model, against interpolation using pixel metrics, directional variograms, spectral band correlation, codispersion and observation coherence. On 735 satellite tiles from 190 scenes, the networks differ by at most 0.03dB and outperform bicubic interpolation by $1.39-1.42$dB at $\times 2$. They recover about 30% of the spectral power above the observation's Nyquist frequency and, at $\times 4$, produce more isotropic fields than the reference (range anisotropy 0.59 against 0.49). Energy-based metrics can favour invented detail. Enforcing coherence reaches the quantisation limit, yet at least 99.7% of the remaining error lies in the observation operator's null space, which coherence cannot constrain. Under spatially blocked, paired evaluation with two backbones and a sharpening control, learned reconstruction changes land-use accuracy by $-1.0$ to $+3.1$ percentage points (significant in 7 of 24 comparisons) and building-change F1 by $-0.015$ to $+0.024$ (none significant) relative to bicubic interpolation, and amplifies spurious change between two observations of an unchanged scene by $1.3-1.5$ times where interpolation attenuates it. Full-resolution JPEG\,2000 matches or exceeds reduced-resolution transmission with reconstruction at every matched bitrate. Learned reconstruction is most useful when only low-resolution imagery is available, and its evaluation should combine pixel metrics with spatial structure, observation coherence and task-level tests under spatial validation.

cs.CV↗

arXiv2026

Semi-Supervised Learning under Spatially Biased Sampling

Standard semi-supervised learning (SSL) typically relies on labelled and unlabelled data sharing a common marginal distribution. This assumption is often violated by biased spatial sampling mechanism, when labels are collected under spatially biased or preferential site selection. We treat this marginal mismatch, spatial autocorrelation, and spatial non-stationarity as three distinct mechanisms, varied independently via a labelled-sampling concentration parameter, a spatial length scale, and a non-stationarity strength parameter, and ask how mismatch degrades SSL, whether the cluster and manifold assumptions survive it, and how the resulting failure can be diagnosed. Using a controlled synthetic framework alongside PovertyMap-WILDS, California housing, socio-economic and US air quality monitoring datasets, we systematically vary the degree of mismatch while accounting for spatial autocorrelation and non-stationarity. Through a series of analyses including a segmented-regression changepoint, we show that in the synthetic generator, SSL performance does not degrade gradually but instead exhibits a threshold-like breakdown between approximately 0.71 and 0.77 once distribution mismatch becomes sufficiently severe. We further demonstrate that spatial non-stationarity contributes to performance loss independently of marginal mismatch and that models become increasingly overconfident outside the regions where labels are available. To support practical deployment, we evaluate several distribution-divergence measures as indicators of reliability and introduce a kernel-weighted local divergence metric that provides a more stable estimate of spatial mismatch than a naïve localised approach. These findings provide empirical evidence and diagnostic tools for better documenting the risk of incorporating unlabelled spatial data into semi-supervised learning workflows.

cs.LG↗