White
papers

Long-form research from EarthScan. Peer-grade methodology, free with email. We publish a few of these a year — when there's something substantive to say.

Whitepaper
05 May 2026
22 pages · 16 min
VeerNet: AI for Raster Well-Log Digitisation
Roughly 60% of the world's producing wells have legacy well-log data trapped behind raster scans that nobody has the time, money, or patience to digitise by hand. VeerNet — published in MDPI Journal of Imaging, 9(7), 136 — is the deep-learning architecture EarthScan built specifically to fix that. Hybrid CNN-residual encoder, transformer self-attention bottleneck, upsampling decoder. F1 = 35%, IoU = 30% on a 10K-image benchmark across five candidate loss functions. Now in production behind ES Raster Digitizer at multiple major operators with ~50× throughput vs interpreter-only baselines. This whitepaper covers the problem, the architecture, the loss-function ablation, the deployment, and what's next.
Whitepaper
03 May 2026
24 pages · 18 min
MIMEA: AI for Biomass and Bio-Inspired Materials
Lignin is the second-most-abundant biopolymer on Earth and the least commercially understood. This whitepaper makes the case for deep learning as the missing layer between heterogeneous biomass feedstock and high-value downstream chemistry. We cover Higher Heating Value prediction without a calorimeter, neural surrogates for atomic-scale potential energy surfaces, the multimodal data infrastructure required, and a sequenced 12-month adoption path. Drawn from the MIMEA feasibility study (Real AI B.V., 2021) and updated with the state of the art in 2026.

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