Official implementation of "A label-efficient remote sensing world model for multimodal data fusion"
The corresponding code will be released.
Classes | Prompts |
---|---|
grass healthy | A hyperspectral and lidar multimodal data of grass healthy |
The grass healthy is next to the road | |
The grass healthy is dark green | |
The spectral value of grass healthy is higher than that of the grass stressed | |
grass stressed | A hyperspectral and lidar multimodal data of grass stressed |
The grass stressed is closer to the road and parking lots | |
The grass stressed is pale green | |
The shape of the grass stressed is irregular | |
grass synthetic | A hyperspectral and lidar multimodal data of grass synthetic |
The grass synthetic is located inside the running track | |
The shape of grass synthetic is a fixed-size rectangle | |
The spectrak value transformation interval of grass synthetic is small | |
trees | A hyperspectral and lidar multimodal data of trees |
The trees beside road | |
The trees appear as small circles | |
Trees are higher than grass | |
soil | A hyperspectral and lidar multimodal data of soil |
The sail is tan | |
The shape of the soil is irregular | |
The surface of soil is not smooth | |
water | A hyperspectral and lidar multimodal data of water |
The water has a smooth surface | |
Trees grew along the water | |
The water appears dark blue or black | |
residential | A hyperspectral and lidar multimodal data of residential |
Residential are densely packed | |
Residential buildings appear as small blocks | |
There are trees near the residential | |
commercial | A hyperspectral and lidar multimodal data of commercial |
The shapes of commercial are inconsistent | |
Commercial appear as large blocks | |
There are parking lot 1 and parking lot 2 near the commercial | |
road | A hyperspectral and lidar multimodal data of road |
Trees grew along the road | |
The road appear as elongated strip shape | |
Roads are narrower than highways and railways | |
highway | A hyperspectral and lidar multimodal data of highway |
The highway is strip-shaped | |
Cars on the highway are discontinuous | |
Highways are wider than railways | |
railway | A hyperspectral and lidar multimodal data of railway |
The railway is strip-shaped | |
The curvature of the railway is smooth | |
Trains on the railway are continuous | |
parking lot 1 | A hyperspectral and lidar multimodal data of parking lot 1 |
The area of parking lot 1 is empty | |
The parking lot 1 is next to the road | |
the parking lot 1 is near buildings | |
parking lot 2 | A hyperspectral and lidar multimodal data of parking lot 2 |
The colors of parking lot 2 are messed up | |
The parking lot 2 is next to the road | |
the parking lot 2 is near buildings | |
tennis court | A hyperspectral and lidar multimodal data of tennis court |
There is also a crimson running track next to the tennis court | |
The height is close to the running track | |
Tennis court is a regular rectangle | |
running track | A hyperspectral and lidar multimodal data of running track |
The running track is an ellipse | |
The running track is crimson | |
There is grass synthetic in the middle of the running track |
Classes | Prompts |
---|---|
grass healthy | A hyperspectral and lidar multimodal data of grass healthy |
The grass healthy is next to the road | |
The grass healthy is dark green | |
The spectral value of grass healthy is higher than that of the grass stressed | |
grass stressed | A hyperspectral and lidar multimodal data of grass stressed |
The grass stressed is closer to the road and parking lots | |
The grass stressed is pale green | |
The shape of the grass stressed is irregular | |
artificial turf | A hyperspectral and lidar multimodal data of artificial turf |
The artificial turf is located inside the running track | |
The shape of artificial turf is a fixed-size rectangle | |
The spectral value transformation interval of artificial turf is small | |
evergreen trees | A hyperspectral and lidar multimodal data of artificial turf |
The evergreen trees beside road | |
The evergreen trees appear as small circles | |
The evergreen trees is dark green | |
deciduous trees | A hyperspectral and lidar multimodal data of deciduous trees |
The trees beside road | |
The trees appear as small circles | |
The deciduous trees is yellowish-brown | |
bare earth | A hyperspectral and lidar multimodal data of bare earth |
The bare earth is tan | |
The shape of the bare earth is irregular | |
The surface of bare earth is not smooth | |
water | A hyperspectral and lidar multimodal data of water |
The water has a smooth surface | |
Trees grew along the water | |
The water appears dark blue or black | |
residential buildings | A hyperspectral and lidar multimodal data of residential buildings |
Residential buildings are densely packed | |
Residential buildings appear as small blocks | |
There are trees near the residential buildings | |
non-residential buildings | A hyperspectral and lidar multimodal data of non-residential buildings |
The shapes of non-residential buildings are inconsistent | |
Non-residential buildings appear as large blocks | |
Both paved and unpaved parking lots near the non-residential buildings | |
roads | A hyperspectral and lidar multimodal data of roads |
Trees grew along the road | |
The road appear as elongated strip shape | |
Roads are narrower than highways and railways | |
sidewalks | A hyperspectral and lidar multimodal data of sidewalks |
Sidewalks are parallel to road and major thoroughfares | |
Sidewalks are located near roads, major thoroughfares, and buildings | |
The distribution of sidewalks is irregular | |
crosswalks | A hyperspectral and lidar multimodal data of crosswalks |
Crosswalks are located above the road and major thoroughfares | |
Crosswalks are perpendicular to roads and major thoroughfares | |
Crosswalks connect two sidewalks | |
major thoroughfares | A hyperspectral and lidar multimodal data of major thoroughfares |
Major thoroughfares are border road and highway | |
Major thoroughfares are wider than road | |
Major thoroughfares are rarely bend | |
highway | A hyperspectral and lidar multimodal data of highway |
The highway is strip-shaped | |
The highway and major thoroughfares cross | |
The highway and railway do not cross | |
railway | A hyperspectral and lidar multimodal data of railway |
The railway is strip-shaped | |
The curvature of the railway is smooth | |
Trains on the railway are continuous | |
paved parking lots | A hyperspectral and lidar multimodal data of paved parking lots |
The paved parking lots are strip shape | |
The paved parking lots are next to the road | |
The paved parking lots are near buildings | |
unpaved parking lots | A hyperspectral and lidar multimodal data of unpaved parking lots |
The colors of parking lot 2 are messed up | |
Unpaved parking lots are next to the road | |
The area of unpaved parking lots is small | |
cars | A hyperspectral and lidar multimodal data of cars |
The cars are next to the paved parking lots | |
The cars are next to the non-residential buildings | |
The cars are discontinuous | |
trains | A hyperspectral and lidar multimodal data of trains |
The trains are strip shape | |
The trains are next to the railway | |
The trains are continuous | |
stadium seats | A hyperspectral and lidar multimodal data of stadium seats |
There is artificial turf in the middle of the stadium seats | |
The height of stadium seats is much higher than that of artificial turf | |
The stadium seats are an ellipse |
Classes | Prompts |
---|---|
tree | A hyperspectral and lidar multimodal data of tree |
The trees beside road | |
The trees appear as small circles | |
Trees are higher than grass | |
mostly grass | A hyperspectral and lidar multimodal data of mostly grass |
The mostly grass is next to the road | |
The grass healthy is green | |
The spectral value of grass healthy is higher than that of the grass stressed | |
mixed ground surface | A hyperspectral and lidar multimodal data of mixed ground surface |
The mixed ground surface is yellow and green | |
The mixed ground surface appears next to the tree | |
The mixed ground surface appears next to the sidewalk | |
dirt and sand | A hyperspectral and lidar multimodal data of dirt and sand |
The bare earth is tan | |
The shape of the dirt and sand is irregular | |
The surface of dirt and sand is not smooth | |
road | A hyperspectral and lidar multimodal data of road |
Trees grew along the road | |
The building and building shadow are next to road | |
The road appear as elongated strip shape | |
water | A hyperspectral and lidar multimodal data of water |
The water has a smooth surface | |
Trees grew along the water | |
The water appears black | |
building shadow | A hyperspectral and lidar multimodal data of building shadow |
The building shadow next to buildings | |
The building shadow appears black | |
The building shadow is behind the building to the right | |
building | A hyperspectral and lidar multimodal data of building |
Building is densely packed | |
Building appears as small blocks | |
There are trees near the building | |
sidewalk | A hyperspectral and lidar multimodal data of sidewalk |
Sidewalk is parallel to road | |
Sidewalk is located near roads and buildings | |
The distribution of sidewalks is irregular | |
yellow curb | A hyperspectral and lidar multimodal data of yellow curb |
Yellow curb is parallel to road | |
Yellow curb is yellow | |
Yellow curb are located near roads and sidewalks | |
cloth panels | A hyperspectral and lidar multimodal data of cloth panels |
Cloth panels are four regular rectangles | |
Cloth panels cover the mixed ground surface | |
Cloth panels are located next to the trees |
The training samples are set following Ref. AM3Net [IEEE TCSVT'2022].