Beyond Algorithms: Hardware-Enabled Innovations in Remote Sensing Payloads, CubeSat Missions and Low-Altitude Applications

Abstract submission deadline

31 October 2026

Manuscript submission deadline

31 December 2026

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Topic Information

Dear Colleagues,

Remote sensing is undergoing a rapid transformation towards miniaturised payloads, agile platforms, and intelligent on-board processing. Advances in sensor design, in-orbit calibration, and multi-platform coordination have created unprecedented opportunities for geosciences, environmental monitoring, urban management, public health, and the emerging low-altitude economy. The continuous evolution of CubeSat technologies, together with innovative airborne and spaceborne payloads, is not only enabling low-cost, high-frequency Earth observation but also fostering novel applications in areas such as precision agriculture, infrastructure monitoring, disaster response, and health geography.

In this Topic, we seek to convene contributions at the frontier of this field that advance the architecture, methodology, and application of next-generation remote and intelligent sensing systems across multiple spatial and technological scales. It spans a continuum from the conception, design, and calibration of innovative sensors and payloads, through satellite and CubeSat missions, to airborne, unmanned aerial, and terrestrial observation networks that together underpin the emerging low-altitude economy. Particular emphasis is placed on the integration of unmanned aerial systems to create agile, reconfigurable, and intelligent platforms that bridge the observational gap between orbital and ground-based sensing, enabling near-real-time data acquisition, edge computing, and multi-scale environmental monitoring. By highlighting the intrinsic synergy among hardware innovation, embedded intelligence, and data-driven analytics, this Topic aims to combine aerospace engineering, electronics, and geomatics into a coherent framework of integrated sensing, fostering new paradigms for urban observability, resilient infrastructure, and sustainable spatial governance while redefining the interface between aerospace capabilities and the complex socio-economic systems of the contemporary world.

We invite the submission of regular articles presenting original research, technical developments, and applied studies. Topics of interest include, but are not limited to, the following:

• Design, development, calibration, and in-orbit validation of novel airborne and spaceborne payloads (multi-/hyperspectral, thermal, LiDAR, radar);
• CubeSat technologies and mission concepts, including payload miniaturisation, intelligent on-board processing, and multi-satellite collaboration;
• Data fusion, physical modelling, and AI-based methods tailored to hardware-enabled, near-real-time, and low-altitude applications;
• Hardware-enabled remote sensing applications: Urban airspace management, smart agriculture, infrastructure monitoring, disaster response, epidemiology, and health geography, all supported by advances in novel payloads, sensors, and edge computing hardware rather than by purely algorithmic innovation;
• Economic and societal applications leveraging novel hardware: Urbanisation monitoring, industrial distribution, and disaster-related economic loss assessment, underpinned by breakthroughs in sensor design, CubeSat platforms, and integrated processing hardware.

Prof. Dr. Shaoju Wang
Prof. Dr. Xiang Zhang
Dr. Mahlatse Kganyago
Dr. Aminjon A. Gulakhmadov
Prof. Dr. Zongxuan Li
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Aerospace aerospace	2.5	4.8	2014	18.5 Days	CHF 2400	Submit
Drones drones	5.2	10.0	2017	21.1 Days	CHF 2600	Submit
Electronics electronics	2.9	7.0	2012	14.8 Days	CHF 2400	Submit
Geomatics geomatics	3.7	4.6	2021	21.6 Days	CHF 1200	Submit
Remote Sensing remotesensing	4.3	9.4	2009	22 Days	CHF 2700	Submit
Sensors sensors	4.0	9.4	2001	17.8 Days	CHF 2600	Submit
Smart Cities smartcities	6.6	13.0	2018	25.1 Days	CHF 2000	Submit

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Published Papers (1 paper)

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Open AccessArticle

Simulated On-Board AI-Based Classification of Radiation-Induced SRAM Event Upsets

by Artur Kazak, Stefan Popa, Andrei Bertescu and Mihai Ivanovici

Electronics 2026, 15(13), 2814; https://doi.org/10.3390/electronics15132814 - 26 Jun 2026

Viewed by 465

Abstract

Radiation monitoring with SRAM-based FPGAs traditionally relies on offset-histogram analysis, which requires a chip-specific calibration campaign at an accelerator before multiple-cell upsets (MCUs) can be discriminated from coincident single-cell upsets (SCUs). The cost and complexity of such calibration restrict the approach to dedicated, [...] Read more.

Radiation monitoring with SRAM-based FPGAs traditionally relies on offset-histogram analysis, which requires a chip-specific calibration campaign at an accelerator before multiple-cell upsets (MCUs) can be discriminated from coincident single-cell upsets (SCUs). The cost and complexity of such calibration restrict the approach to dedicated, beam-test-funded programs. We propose an AI-based on-board classifier that achieves MCU/SCU discrimination directly, without any chip-specific calibration. A lightweight Multi-Layer Perceptron (MLP), trained entirely on synthetic data covering five representative bit-interleaving layouts, is integrated on an AMD Artix-7 XC7A200T FPGA together with per-detection-element telemetry aggregation. The classifier achieves F1 = 0.92–0.97 on structured BRAM layouts when per-chip calibration data are available (calibrated ceiling) and, without any chip-specific calibration, retains F1 up to 0.81 ± 0.02 (held-out, mean over five seeds) on previously unseen layouts with near-perfect recall. A sensitivity analysis across a $20\times$ range of SEU rates and a $4\times$ range of MCU fractions confirms the robustness of the proposed approach. A feature-ablation study identifies an indispensable feature subset, while a comparative evaluation of four alternative classifier architectures (decision tree, support vector machine (SVM), two MLP variants) establishes the reference MLP as the optimal choice. Post-implementation results on the Artix-7 200T show that the MLP-enhanced and calibrated-histogram designs occupy nearly identical FPGA footprints, reframing the choice between them as an operational decision driven by calibration availability rather than by hardware cost. Full article

(This article belongs to the Topic Beyond Algorithms: Hardware-Enabled Innovations in Remote Sensing Payloads, CubeSat Missions and Low-Altitude Applications)

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