LALE: Lightweight-Transformer Architecture for Land-Cover Estimation
Semantic segmentation of remote sensing imagery requires models that capture both global context and local detail under tight computational budgets. Prior work typically optimizes for one of these axes: attention for global context, convolution for local detail, or compactness for efficiency. While hybrid approaches aim to capture both, they require architectural changes and encoder backbones with computational overhead, limiting efficiency and performance. We present LALE (Lightweight-transformer Architecture for Land-cover Estimation), an end-to-end remote sensing image segmentation architecture, that bifurcates its encoder by resolution: lightweight ConvMixer stages handle high-resolut...
| Authors | Ümit Mert Çağlar, Alptekin Temizel |
|---|---|
| Primary category | eess.IV |
| Published | 1 June 2026 |
| Last updated at source | 18 June 2026 |
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