Reuse out-of-year data to enhance land cover mappingvia feature
  disentanglement and contrastive learning
Cassio F. Dantas Raffaele Gaetano Claudia Paris Dino Ienco
Timely up-to-date land use/land cover (LULC) maps play a pivotal role in
supporting agricultural territory management, environmental monitoring and
facilitating well-informed and sustainable decision-making. Typically, when
creating a land cover (LC) map, precise ground truth data is collected through
time-consuming and expensive field campaigns. This data is then utilized in
conjunction with satellite image time series (SITS) through advanced machine
learning algorithms to get the final map. Unfortunately, each time this process
is repeated (e.g., annually over a region to estimate agricultural production
or potential biodiversity loss), new ground truth data must be collected,
leading to the complete disregard of previously gathered reference data despite
the substantial financial and time investment they have required. How to make
value of historical data, from the same or similar study sites, to enhance the
current LULC mapping process constitutes a significant challenge that could
enable the financial and human-resource efforts invested in previous data
campaigns to be valued again. Aiming to tackle this important challenge, we
here propose a deep learning framework based on recent advances in domain
adaptation and generalization to combine remote sensing and reference data
coming from two different domains (e.g. historical data and fresh ones) to
ameliorate the current LC mapping process. Our approach, namely REFeD (data
Reuse with Effective Feature Disentanglement for land cover mapping), leverages
a disentanglement strategy, based on contrastive learning, where invariant and
specific per-domain features are derived to recover the intrinsic information
related to the downstream LC mapping task and alleviate possible distribution
shifts between domains. Additionally, REFeD is equipped with an effective
supervision scheme where feature disentanglement is further enforced via
multiple levels of supervision at different granularities. The experimental
assessment over two study areas covering extremely diverse and contrasted
landscapes, namely Koumbia (located in the West-Africa region, in Burkina Faso)
and Centre Val de Loire (located in centre Europe, France), underlines the
quality of our framework and the obtained findings demonstrate that out-of-year
information coming from the same (or similar) study site, at different periods
of time, can constitute a valuable additional source of information to enhance
the LC mapping process.