  Land-cover classification has long been a hot and difficult challenge in
remote sensing community. With massive High-resolution Remote Sensing (HRS)
images available, manually and automatically designed Convolutional Neural
Networks (CNNs) have already shown their great latent capacity on HRS
land-cover classification in recent years. Especially, the former can achieve
better performance while the latter is able to generate lightweight
architecture. Unfortunately, they both have shortcomings. On the one hand,
because manual CNNs are almost proposed for natural image processing, it
becomes very redundant and inefficient to process HRS images. On the other
hand, nascent Neural Architecture Search (NAS) techniques for dense prediction
tasks are mainly based on encoder-decoder architecture, and just focus on the
automatic design of the encoder, which makes it still difficult to recover the
refined mapping when confronting complicated HRS scenes.
  To overcome their defects and tackle the HRS land-cover classification
problems better, we propose AutoLC which combines the advantages of two
methods. First, we devise a hierarchical search space and gain the lightweight
encoder underlying gradient-based search strategy. Second, we meticulously
design a lightweight but top-performing decoder that is adaptive to the
searched encoder of itself. Finally, experimental results on the LoveDA
land-cover dataset demonstrate that our AutoLC method outperforms the
state-of-art manual and automatic methods with much less computational
consumption.
