// Earth Observation Network · Active
Our model is trained on EuroSAT, a benchmark dataset built entirely from Sentinel-2 satellite imagery captured over Europe. Each image is stored as a multi-band .tif file, preserving the full radiometric depth across all 13 spectral bands — from visible light through shortwave infrared.
The dataset contains 20,000 geo-referenced image patches, each 64×64 pixels, covering 10 distinct land-use and land-cover classes. It is perfectly balanced, with 2,000 samples per class, ensuring no category dominates training and that the model learns each terrain type with equal fidelity.
Working with raw .tif files allows us to retain all spectral information discarded by conventional RGB formats — enabling the classifier to detect subtle signatures invisible to the human eye, such as vegetation stress, soil moisture, and urban heat variance.
// 10 Land-Use Classes · 2,000 Images Each
// Methodology
// Pre-processing
Each .tif image carries 13 raw spectral bands with pixel values spanning different physical ranges depending on wavelength. Before any learning can occur, all bands are normalised per-channel using min-max scaling, squeezing every value into [0, 1] while preserving relative spectral contrast. Statistics are computed on the training split only and applied identically to validation and test sets, preventing any form of data leakage across partitions.
// Dimensionality Reduction
With 13 correlated spectral bands, redundancy is inevitable — neighbouring infrared channels in particular share significant variance. Principal Component Analysis was applied across the flattened band space, retaining the 8 components that collectively explain the dominant variance in the data. This reduction compresses the input without discarding discriminative spectral signal, accelerates training, and helps regularise against overfitting on high-dimensional per-pixel features.
// Architecture Search
Four pre-trained convolutional architectures were benchmarked under identical training conditions — same optimiser, same learning rate schedule, same augmentation policy — to isolate the effect of architecture alone. The candidates spanned a wide capacity range: AlexNet (lightweight, ~61M parameters, deep linear head), GoogLeNet (Inception modules, multi-scale receptive fields), ResNet-50 (residual skip connections, 50 layers), and EfficientNet (compound scaling, state-of-the-art efficiency). All models were fine-tuned from ImageNet weights with only the final classification head replaced for 10 EuroSAT classes.
// Result
Against expectation, AlexNet outperformed all three larger architectures on the EuroSAT benchmark. Its shallower depth and wider fully-connected layers proved well-suited to the relatively uniform 64×64 patch structure of the dataset, where the spatial patterns are coarser than natural ImageNet scenes. The winning configuration used RMSprop (lr = 0.0001, momentum = 0.9, batch size = 32) over 8 epochs, achieving a final validation accuracy of 91.80% — climbing steadily from 83.85% at epoch 1 to peak performance at epoch 8.
// AlexNet · Training Log · Best Trial
// Model Comparison · Same Hyperparameters
91.80% ★ Selected
Lightweight deep head, ideal for 64×64 coarse patch structure. Fast convergence, minimal overfitting.
Inception multi-scale modules. Strong on complex scenes, but over-parametrised for small uniform patches.
Skip connections mitigate vanishing gradients across 50 layers. Competitive but slower to converge on this scale.
Compound scaling across depth, width, and resolution. Highly efficient on large inputs; overhead outweighed gains at 64px.
// Live Inference
Upload a Sentinel-2 .tif patch and the AlexNet model will classify it into one of 10 EuroSAT land-use categories in real time.
DROP .TIF FILE HERE
OR CLICK TO BROWSE
AWAITING INPUT SIGNAL
Behind every orbital data stream is a team of scientists, engineers, and visionaries who believe that seeing Earth more clearly makes protecting it possible.
Astrophysicist turned space entrepreneur. Former NASA Earth Science Division Director. 20 years in orbital mechanics and satellite design.
Astrophysicist turned space entrepreneur. Former NASA Earth Science Division Director. 20 years in orbital mechanics and satellite design.
Climate scientist and remote sensing expert. Her research shaped IPCC satellite data protocols. Leads our global environmental monitoring programs.
Machine learning pioneer with a geospatial AI background. Built the algorithms turning raw satellite pixels into actionable intelligence within seconds of capture.
Former ESA mission controller. Oversees our 24/7 ground station network across 6 continents, ensuring zero-downtime data pipeline operations globally.