Updating a neural network running on an edge device can be quite challenging, especially when the device uses a low bandwidth uplink communication channel. For that reason, sending all the model weights becomes prohibitive. An example of such a device is an Earth Observation (EO) satellite, which employs Convolutional Neural Networks (CNNs) to make decisions related to the images captured using its EO sensors. A possible approach to efficiently update these kinds of devices is to leverage the fact that the weights which are currently used by the edge device are known and update only a subset of them. In this work, several criteria for choosing the subset of weights which are updated have been compared. Since our main focus is to efficiently update models running on EO satellites, the datasets which have been used to test the methods correspond to images taken by these kinds of satellites. The results show that the best approach is Deep Partial Update (DPU). Specifically, the fully fine-tuned model achieves a Dice score of 0.6641 but if DPU is applied in a setting where 25% the weights are allowed to change, the partially updated model gives a Dice score of 0.6391.