Zero-Shot Multi-Spectral Learning: Reimagining a Generalist Multimodal
  Gemini 2.5 Model for Remote Sensing Applications
Multi-spectral imagery plays a crucial role in diverse Remote Sensing
applications including land-use classification, environmental monitoring and
urban planning. These images are widely adopted because their additional
spectral bands correlate strongly with physical materials on the ground, such
as ice, water, and vegetation. This allows for more accurate identification,
and their public availability from missions, such as Sentinel-2 and Landsat,
only adds to their value. Currently, the automatic analysis of such data is
predominantly managed through machine learning models specifically trained for
multi-spectral input, which are costly to train and support. Furthermore,
although providing a lot of utility for Remote Sensing, such additional inputs
cannot be used with powerful generalist large multimodal models, which are
capable of solving many visual problems, but are not able to understand
specialized multi-spectral signals.
  To address this, we propose a training-free approach which introduces new
multi-spectral data in a Zero-Shot-only mode, as inputs to generalist
multimodal models, trained on RGB-only inputs. Our approach leverages the
multimodal models' understanding of the visual space, and proposes to adapt to
inputs to that space, and to inject domain-specific information as instructions
into the model. We exemplify this idea with the Gemini2.5 model and observe
strong Zero-Shot performance gains of the approach on popular Remote Sensing
benchmarks for land cover and land use classification and demonstrate the easy
adaptability of Gemini2.5 to new inputs. These results highlight the potential
for geospatial professionals, working with non-standard specialized inputs, to
easily leverage powerful multimodal models, such as Gemini2.5, to accelerate
their work, benefiting from their rich reasoning and contextual capabilities,
grounded in the specialized sensor data.
68d350120e215259d193b1b7
Ganesh Mallya
admin_assigned
2025-09-24T15:33:44.714Z
67b500597fa5921119aa5358
/avatars/33c283de346ec83f6ddc98d6d3b8538d.svg
Ganesh Mallya
user
gmallya
68d350120e215259d193b1b8
Yotam Gigi
68d350120e215259d193b1b9
Dahun Kim
admin_assigned
2025-09-24T15:33:55.555Z
6461d65b5ea1bc9a9da98b88
/avatars/fd6e3715ca8f918ca074356244bb9ce9.svg
Dahun Kim
user
mcahny
68d350120e215259d193b1ba
Maxim Neumann
68d350120e215259d193b1bb
Genady Beryozkin
admin_assigned
2025-09-24T15:34:07.003Z
6763fb464cbdc8085793d563
/avatars/2b68f44da4379052ed87ed3970acf43e.svg
Genady Beryozkin
user
genadyb
68d350120e215259d193b1bc
Tomer Shekel
68d350120e215259d193b1bd
Anelia Angelova
Zero-Shot Multi-Spectral Learning: Reimagining a Generalist Multimodal
  Gemini 2.5 Model for Remote Sensing Applications
Multi-spectral imagery plays a crucial role in diverse Remote Sensing
applications including land-use classification, environmental monitoring and
urban planning. These images are widely adopted because their additional
spectral bands correlate strongly with physical materials on the ground, such
as ice, water, and vegetation. This allows for more accurate identification,
and their public availability from missions, such as Sentinel-2 and Landsat,
only adds to their value. Currently, the automatic analysis of such data is
predominantly managed through machine learning models specifically trained for
multi-spectral input, which are costly to train and support. Furthermore,
although providing a lot of utility for Remote Sensing, such additional inputs
cannot be used with powerful generalist large multimodal models, which are
capable of solving many visual problems, but are not able to understand
specialized multi-spectral signals.
  To address this, we propose a training-free approach which introduces new
multi-spectral data in a Zero-Shot-only mode, as inputs to generalist
multimodal models, trained on RGB-only inputs. Our approach leverages the
multimodal models' understanding of the visual space, and proposes to adapt to
inputs to that space, and to inject domain-specific information as instructions
into the model. We exemplify this idea with the Gemini2.5 model and observe
strong Zero-Shot performance gains of the approach on popular Remote Sensing
benchmarks for land cover and land use classification and demonstrate the easy
adaptability of Gemini2.5 to new inputs. These results highlight the potential
for geospatial professionals, working with non-standard specialized inputs, to
easily leverage powerful multimodal models, such as Gemini2.5, to accelerate
their work, benefiting from their rich reasoning and contextual capabilities,
grounded in the specialized sensor data.
A training-free method enables generalist multimodal models to process multi-spectral imagery in a zero-shot manner, enhancing performance on remote sensing tasks.
multi-spectral imagery remote sensing land-use classification environmental monitoring urban planning machine learning models multimodal models zero-shot learning Gemini2.5 land cover land use classification