Data Labeling & Annotation – Weather Data Fusion (Academic)
OtherClassification
I fused and aligned heterogeneous weather data sources (IMD, NOAA, ERA5), performing spatial labeling and object categorization to resolve grid-resolution mismatches. I built CNN and LSTM models from scratch in NumPy, deeply segmenting time series into discrete temporal boundaries as part of the annotation process. All decisions and edge-case handling were documented to ensure modeling reproducibility for data labeling tasks.
• Integrated three sources with precision for consistent spatial categorization.
• Applied sequence segmentation to identify action start/end times in labeled datasets.
• Emphasized structured documentation for clarity and reproducibility.
• Demonstrated relevance to robotics-style spatial annotation and labeling.
I fused and aligned heterogeneous weather data sources (IMD, NOAA, ERA5), performing spatial labeling and object categorization to resolve grid-resolution mismatches. I built CNN and LSTM models from scratch in NumPy, deeply segmenting time series into discrete temporal boundaries as part of the annotation process. All decisions and edge-case handling were documented to ensure modeling reproducibility for data labeling tasks. • Integrated three sources with precision for consistent spatial categorization. • Applied sequence segmentation to identify action start/end times in labeled datasets. • Emphasized structured documentation for clarity and reproducibility. • Demonstrated relevance to robotics-style spatial annotation and labeling.
2025 - Present