Zobrazeno 1 - 10
of 61
pro vyhledávání: '"Albrecht, Conrad M."'
Autor:
Braham, Nassim Ait Ali, Albrecht, Conrad M, Mairal, Julien, Chanussot, Jocelyn, Wang, Yi, Zhu, Xiao Xiang
Foundation models have triggered a paradigm shift in computer vision and are increasingly being adopted in remote sensing, particularly for multispectral imagery. Yet, their potential in hyperspectral imaging (HSI) remains untapped due to the absence
Externí odkaz:
http://arxiv.org/abs/2408.08447
Self-supervised pretraining on large-scale satellite data has raised great interest in building Earth observation (EO) foundation models. However, many important resources beyond pure satellite imagery, such as land-cover-land-use products that provi
Externí odkaz:
http://arxiv.org/abs/2405.20462
Local climate zones (LCZs) established a standard classification system to categorize the landscape universe for improved urban climate studies. Existing LCZ mapping is guided by human interaction with geographic information systems (GIS) or modelled
Externí odkaz:
http://arxiv.org/abs/2405.13993
Preservation of the Nasca geoglyphs at the UNESCO World Heritage Site in Peru is urgent as natural and human impact accelerates. More frequent weather extremes such as flashfloods threaten Nasca artifacts. We demonstrate that runoff models based on (
Externí odkaz:
http://arxiv.org/abs/2405.11814
While the volume of remote sensing data is increasing daily, deep learning in Earth Observation faces lack of accurate annotations for supervised optimization. Crowdsourcing projects such as OpenStreetMap distribute the annotation load to their commu
Externí odkaz:
http://arxiv.org/abs/2403.01641
Compared to supervised deep learning, self-supervision provides remote sensing a tool to reduce the amount of exact, human-crafted geospatial annotations. While image-level information for unsupervised pretraining efficiently works for various classi
Externí odkaz:
http://arxiv.org/abs/2402.16164
Self-supervised learning guided by masked image modelling, such as Masked AutoEncoder (MAE), has attracted wide attention for pretraining vision transformers in remote sensing. However, MAE tends to excessively focus on pixel details, thereby limitin
Externí odkaz:
http://arxiv.org/abs/2310.18653
Autor:
Wang, Yi, Albrecht, Conrad M, Braham, Nassim Ait Ali, Liu, Chenying, Xiong, Zhitong, Zhu, Xiao Xiang
The increasing availability of multi-sensor data sparks wide interest in multimodal self-supervised learning. However, most existing approaches learn only common representations across modalities while ignoring intra-modal training and modality-uniqu
Externí odkaz:
http://arxiv.org/abs/2309.05300
Autor:
Wang, Yi, Liu, Chenying, Tiwari, Arti, Silver, Micha, Karnieli, Arnon, Zhu, Xiao Xiang, Albrecht, Conrad M
Discovering ancient agricultural terraces in desert regions is important for the monitoring of long-term climate changes on the Earth's surface. However, traditional ground surveys are both costly and limited in scale. With the increasing accessibili
Externí odkaz:
http://arxiv.org/abs/2308.02225
Autor:
Pande, Shivam, Braham, Nassim Ait Ali, Wang, Yi, Albrecht, Conrad M, Banerjee, Biplab, Zhu, Xiao Xiang
Hyperspectral image (HSI) classification is gaining a lot of momentum in present time because of high inherent spectral information within the images. However, these images suffer from the problem of curse of dimensionality and usually require a larg
Externí odkaz:
http://arxiv.org/abs/2306.10955