Fangxiao Lu

12 papers A* 1C 2Journal 7Unranked 2
YearRankTypeTitle / Venue / Authors
2026 J jnl
Toward Complex Backgrounds: A Unified Difference-Aware Decoder for Binary Segmentation.
IEEE Trans. Circuits Syst. Video Technol.
Jiepan Li, Wei He, Fangxiao Lu, Hongyan Zhang
2025 J jnl
FS
IEEE Trans. Geosci. Remote. Sens.
Guorong Zhang, Tao Sun, Fangxiao Lu, Shaoquan Zhang, Jie Yin, Yuhao Wu, Zhengqiang Xiong
2025 J jnl
KDGraph: A Keypoint Detection Method for Road Graph Extraction From Remote Sensing Images.
IEEE Trans. Geosci. Remote. Sens.
Wei He, Ruoyao Xue, Fangxiao Lu, Jinjun Xu, Hongyan Zhang
2024 J jnl
Cross-City Semantic Segmentation (C2Seg) in Multimodal Remote Sensing: Outcome of the 2023 IEEE WHISPERS C2Seg Challenge.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens.
Yuheng Liu, Ye Wang, Yifan Zhang, Shaohui Mei, Jiaqi Zou, Zhuohong Li, Fangxiao Lu, Wei He, Hongyan Zhang, Huilin Zhao, Chuan Chen, Cong Xia, Hao Li, Gemine Vivone, Ronny Hänsch, Gülsen Taskin Kaya, Jing Yao, A. K. Qin, Bing Zhang, Jocelyn Chanussot, Danfeng Hong
2024 — conf
Generalized Few-Shot Meets Remote Sensing: Discovering Novel Classes in Land Cover Mapping via Hybrid Semantic Segmentation Framework.
CVPR Workshops
Zhuohong Li, Fangxiao Lu, Jiaqi Zou, Lei Hu, Hongyan Zhang
2024 J jnl
Generalized Few-Shot Meets Remote Sensing: Discovering Novel Classes in Land Cover Mapping via Hybrid Semantic Segmentation Framework.
CoRR
Zhuohong Li, Fangxiao Lu, Jiaqi Zou, Lei Hu, Hongyan Zhang
2024 A* conf
Learning without Exact Guidance: Updating Large-Scale High-Resolution Land Cover Maps from Low-Resolution Historical Labels.
CVPR
Zhuohong Li, Wei He, Jiepan Li, Fangxiao Lu, Hongyan Zhang
2024 J jnl
Learning without Exact Guidance: Updating Large-scale High-resolution Land Cover Maps from Low-resolution Historical Labels.
CoRR
Zhuohong Li, Wei He, Jiepan Li, Fangxiao Lu, Hongyan Zhang
2023 — conf
Multimodal Unsupervised Domain Adaptation for Remote Sensing Image Segmentation.
WHISPERS
Jiaqi Zou, Zhuohong Li, Fangxiao Lu, Wei He, Hongyan Zhang
2022 C conf
Multi-Stage Pseudo-Label Iteration Framework for Semi-Supervised Land-Cover Mapping.
IGARSS
Zhuohong Li, Jiaqi Zou, Fangxiao Lu, Hongyan Zhang
2022 J jnl
The Outcome of the 2021 IEEE GRSS Data Fusion Contest - Track MSD: Multitemporal Semantic Change Detection.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens.
Zhuohong Li, Fangxiao Lu, Hongyan Zhang, Lilin Tu, Jiayi Li, Xin Huang, Caleb Robinson, Nikolay Malkin, Nebojsa Jojic, Pedram Ghamisi, Ronny Hänsch, Naoto Yokoya
2021 C conf
Change Cross-Detection Based on Label Improvements and Multi-Model Fusion for Multi-Temporal Remote Sensing Images.
IGARSS
Zhuohong Li, Fangxiao Lu, Hongyan Zhang, Guangyi Yang, Liangpei Zhang

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

In 2025, he co-authored Assessing the Effectiveness of the Tigress Obfuscator Against MOPSA and BinaryNinja, published at CheckMATE. The paper evaluates the effectiveness of obfuscation techniques and their impact on source-level static analysis and binary-level decompilation.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.