  In this paper, we present a lightweight and effective change detection model,
called TinyCD. This model has been designed to be faster and smaller than
current state-of-the-art change detection models due to industrial needs.
Despite being from 13 to 140 times smaller than the compared change detection
models, and exposing at least a third of the computational complexity, our
model outperforms the current state-of-the-art models by at least $1\%$ on both
F1 score and IoU on the LEVIR-CD dataset, and more than $8\%$ on the WHU-CD
dataset. To reach these results, TinyCD uses a Siamese U-Net architecture
exploiting low-level features in a globally temporal and locally spatial way.
In addition, it adopts a new strategy to mix features in the space-time domain
both to merge the embeddings obtained from the Siamese backbones, and, coupled
with an MLP block, it forms a novel space-semantic attention mechanism, the Mix
and Attention Mask Block (MAMB). Source code, models and results are available
here: https://github.com/AndreaCodegoni/Tiny_model_4_CD
