Bo Peng

PAII Inc. (Ping An U.S. Research Lab)

Advances in deep learning and computer vision are making significant contributions to flood mapping, particularly when integrated with remotely sensed data. Although existing supervised methods, especially deep convolutional neural networks, have proved to be effective, they require intensive manual labeling of flooded pixels to train a multi-layer...

Individual daily travel activities (e.g., work, eating) are identified with various machine learning models (e.g., Bayesian Network, Random Forest) for understanding people’s frequent travel purposes. However, labor-intensive engineering work is often required to extract effective features. Additionally, features and models are mostly calibrated fo...

Earth observation satellites have been continuously monitoring the earth environment for years at different locations and spectral bands with different modalities. Due to complex satellite sensing conditions (e.g., weather, cloud, atmosphere, orbit), some observations for certain modalities, bands, locations, and times may not be available. The Mul...

The MultiEarth 2022 Image-to-Image Translation challenge provides a well-constrained test bed for generating the corresponding RGB Sentinel-2 imagery with the given Sentinel-1 VV & VH imagery. In this challenge, we designed various generation models and found the SPADE [1] and pix2pixHD [2] models could perform our best results. In our self-evaluat...

Near realtime flood mapping in densely-populated urban areas is critical for emergency response. The strong heterogeneity of urban areas poses a big challenge for accurate near realtime flood mapping. However, previous studies on automatic methods for urban flood mapping perform infeasible in near realtime or fail to generalize well to other floods...

Urban flood mapping is essential for disaster rescue and relief missions, reconstruction efforts, and financial loss evaluation. Much progress has been made to map the extent of flooding with multi-source remote sensing imagery and pattern recognition algorithms. However, urban flood mapping at high spatial resolution remains a major challenge due...

Natural hazards have been resulting in severe damage to our cities, and flooding is one of the most disastrous in the U.S and worldwide. Therefore, it is critical to develop efficient methods for risk and damage assessments after natural hazards, such as flood depth estimation. Existing works primarily leverage photos and images capturing flood sce...

Urban flooding is a major natural disaster that poses a serious threat to the urban environment. It is highly demanded that the flood extent can be mapped in near real-time for disaster rescue and relief missions, reconstruction efforts, and financial loss evaluation. Many efforts have been taken to identify the flooding zones with remote sensing d...

Anomaly detection is one of the most important issues in hyperspectral remote sensing. However, traditional anomaly detection algorithms cannot be used for onboard real-time processing due to heavy computational load caused by the dimensionality curse of hyperspectral data. To implement onboard real-time hyperspectral anomaly detection, the followi...

Real-time processing of anomaly detection has become one of the most important issues in hyperspectral remote sensing. Due to the fact that most widely used hyperspectral imaging spectrometers work in a pushbroom fashion, it is necessary to process the incoming data line in a causal linewise progressive manner with no future data involved. In this...

Crop pests and diseases is one of major agricultural disasters, which have caused heavy losses in agricultural production each year. Hyperspectral remote sensing technology is one of the most advanced and effective method for monitoring crop pests and diseases. However, Hyperspectral facing serial problems such as low degree of automation of data p...