Transformer

The Transformer is a neural networkNeural NetworkA Neural Network is a computing system inspired by the structure of biological neural networks in the brain. It forms... architecture introduced in the landmark 2017 paper "Attention Is All You Need" that relies entirely on self-attention mechanisms rather than recurrence or convolution to model relationships between elements in a sequence. By computing attention weights between all pairs of positions simultaneously, Transformers capture long-range dependencies more effectively than RNNs and process sequences in parallel rather than sequentially. The architecture consists of encoder and decoder blocks, each containing multi-head self-attention layers and feedforward networks, with residual connections and layer normalization. Transformers in Geospatial AI ApplicationsTransformers are rapidly transforming geospatial analysisGeospatial AnalysisGeospatial analysis applies statistical methods and specialized software to interpret spatial data, uncovering patter... across multiple domains. Vision Transformers (ViT) divide satellite images into patches and process them as sequences, achieving state-of-the-art accuracy on remote sensingRemote SensingRemote sensing is the science of collecting data about Earth's surface without direct physical contact, primarily usi... classification benchmarks. Temporal Transformers model multi-date satellite image sequences for change detectionChange DetectionChange detection uses geospatial data and imagery to track and analyze alterations in landscapes, infrastructure, or ... and crop monitoringCrop MonitoringCrop Monitoring uses satellite imagery and remote sensing to track crop growth, health, and stress conditions through..., capturing seasonal patterns across entire growing seasons. Large language models built on Transformer architectures enable natural language interfaces to GISGISGeographic Information Systems (GIS) enable users to analyze and visualize spatial data to uncover patterns, relation... systems and automated interpretation of geospatial dataGeospatial DataGeospatial data encompasses information about the location, shape, and relationships of physical features on Earth. I.... Multi-modal Transformers jointly process satellite imagerySatellite ImagerySatellite imagery consists of photographs and data captured by Earth observation satellites orbiting the planet. Thes..., text descriptions, and tabular data for holistic geospatial understanding. Foundation models for Earth observationFoundation Models for Earth ObservationFoundation Models for Earth Observation are large-scale AI models pretrained on vast amounts of satellite and geospat... increasingly adopt Transformer architectures for their scalability and representation learning capabilities. Advantages and Computational TradeoffsTransformers excel at capturing global context across entire images or sequences, which is valuable for understanding large-scale spatial patterns. Their parallel processing enables efficient training on modern GPU hardware. However, the self-attention mechanismAttention MechanismThe Attention Mechanism is a neural network component that learns to assign different weights to different parts of t... has quadratic memory and computation complexity with respect to sequence length, which can be challenging for very high-resolution satellite imagery. Efficient Transformer variants with linear attention, sparse attention, or hierarchical processing address these scaling challenges while retaining the benefits of attention-based modeling.