遥感图像分割从业者终于有了兼顾精度和效率的轻量方案——LALE用1.6M参数达到接近大模型的效果，做边缘部署或实时处理的团队可以直接参考。
LALE 是一种专为遥感图像语义分割设计的轻量级Transformer架构，通过分辨率分叉编码器：轻量ConvMixer处理高分辨率局部特征，Transformer处理低分辨率全局上下文，将自注意力的二次复杂度限制在深层下采样特征图上。全MLP多尺度解码器结合RMSNorm和StarReLU进一步降低计算量和参数量。在ARAS400k基准上，最小变体（仅1.6M参数）在F1分数上仅比最佳基线UPerNet低2.6点，但参数量减少4.5倍，存储减少7倍，计算量减少17倍，吞吐量提升1.8倍。该工作为资源受限场景下的遥感分割提供了高效解决方案。
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-resolution local features, while transformer stages handle low-resolution global context, confining the quadratic cost of self-attention to deep, downsampled feature maps. An all-MLP multi-scale decoder, together with RMSNorm and StarReLU throughout, further reduces compute and parameter count. On the large-scale ARAS400k remote-sensing segmentation benchmark, LALE establishes a strong efficiency-performance trade-off against CNN, transformer, and hybrid baselines. Our smallest variant, (just 1.6M parameters), reaches within 2.6 F1 points of the best baseline (UPerNet) while using 4.5x fewer parameters, 7x less storage, 17x fewer GMACs, and delivering 1.8x higher throughput.